pacman::p_load(tidyverse, janitor, ppcor, ltm, pastecs, corrtable, psych)
options(scipen=999)
data <- read.csv("data2.csv")
data <- data[, -1] # Removed x column that appears when exporting a csv from RAnalyses (09/23/24)
Recodes and Scales
Dummy Codes
These numbers contain the missing values, to get sample descriptive (e.g., race/ethnicity) the numbers must be calculated with the data frames in the Wrangling section. in other words, run all chunks then run tabyl(FSFSurveyT1$race_eth) in the console.
# Sex
data$Genderfactor <-as.factor(data$Gender)
data$GenderNumb <-as.numeric(data$Genderfactor)
table(data$GenderNumb)
1 2
80 26 data$Female <- data$GenderNumb
data$Female = ifelse(data$GenderNumb == 1, 1, data$Female)
data$Female = ifelse(data$GenderNumb == 2, 0, data$Female)
table(data$Female)
0 1
26 80 table(data$GenderNumb)
1 2
80 26 data$Male <- data$GenderNumb
data$Male = ifelse(data$GenderNumb == 1, 0, data$Male)
data$Male = ifelse(data$GenderNumb == 2, 1, data$Male)
table(data$Male)
0 1
80 26 # Race
# Dem percents
table(data$race_eth)
American Indian/Native American Asian or Pacific Islander
1 6
Black/African American Hispanic
10 26
Multiracial Other
4 1
White
58 data$race_eth1 <- as.factor(data$race_eth)
data$race_eth2 <- as.numeric(data$race_eth1)
table(data$race_eth2)
1 2 3 4 5 6 7
1 6 10 26 4 1 58 data$race_ethDC <- data$race_eth2
data$race_ethDC = ifelse(data$race_eth2 == 7, 8, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 6, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 5, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 4, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 3, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 2, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 1, 0, data$race_ethDC)
table(data$race_ethDC)
0 8
48 58 data$White = data$race_ethDC
data$White <- as.factor(data$White)
data$White = ifelse(data$race_ethDC == 8, 1, data$White)
data$White = ifelse(data$race_ethDC == 0, 0, data$White)
table(data$White)
0 1
48 58 SRP Reverse Codes
IPM (16, 24, 31, 38, 61) CA (11, 19, 23, 26, 44) ELS (14, 22, 25, 36, 47) ASB (5, 6, 18, 21, 34, 46)
Citation: Paulhus, D.L., Neumann, C. S., & Hare, R.D. (in press). Manual for the Self-Report Psychopathy scale 4th edition. Toronto: Multi-Health Systems.
# IPM
table(data$SRP_16n)
1 2 3 4 5
2 18 36 33 17 data$SRP16nRev = data$SRP_16n
data$SRP16nRev = ifelse(data$SRP_16n == 1, 5, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 2, 4, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 4, 2, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 5, 1, data$SRP16nRev)
table(data$SRP16nRev)
1 2 3 4 5
17 33 36 18 2 table(data$SRP_24n)
1 2 3 4 5
5 19 24 46 12 data$SRP24nRev = data$SRP_24n
data$SRP24nRev = ifelse(data$SRP_24n == 1, 5, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 2, 4, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 4, 2, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 5, 1, data$SRP24nRev)
table(data$SRP24nRev)
1 2 3 4 5
12 46 24 19 5 table(data$SRP_31n)
1 2 3 4 5
6 15 46 30 9 data$SRP31nRev = data$SRP_31n
data$SRP31nRev = ifelse(data$SRP_31n == 1, 5, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 2, 4, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 4, 2, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 5, 1, data$SRP31nRev)
table(data$SRP31nRev)
1 2 3 4 5
9 30 46 15 6 table(data$SRP_38n)
1 2 3 4 5
4 18 28 36 20 data$SRP38nRev = data$SRP_38n
data$SRP38nRev = ifelse(data$SRP_38n == 1, 5, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 2, 4, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 4, 2, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 5, 1, data$SRP38nRev)
table(data$SRP38nRev)
1 2 3 4 5
20 36 28 18 4 table(data$SRP_61n)
1 2 3 4 5
5 14 15 37 35 data$SRP61nRev = data$SRP_61n
data$SRP61nRev = ifelse(data$SRP_61n == 1, 5, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 2, 4, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 4, 2, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 5, 1, data$SRP61nRev)
table(data$SRP61nRev)
1 2 3 4 5
35 37 15 14 5 # CA
table(data$SRP_11n)
1 2 3 4 5
2 7 10 43 44 data$SRP11nRev = data$SRP_11n
data$SRP11nRev = ifelse(data$SRP_11n == 1, 5, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 2, 4, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 4, 2, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 5, 1, data$SRP11nRev)
table(data$SRP11nRev)
1 2 3 4 5
44 43 10 7 2 table(data$SRP_19n)
2 3 4 5
8 14 54 29 data$SRP19nRev = data$SRP_19n
data$SRP19nRev = ifelse(data$SRP_19n == 1, 5, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 2, 4, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 4, 2, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 5, 1, data$SRP19nRev)
table(data$SRP19nRev)
1 2 3 4
29 54 14 8 table(data$SRP_23n)
1 2 3 4 5
28 37 15 14 12 data$SRP23nRev = data$SRP_23n
data$SRP23nRev = ifelse(data$SRP_23n == 1, 5, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 2, 4, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 4, 2, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 5, 1, data$SRP23nRev)
table(data$SRP23nRev)
1 2 3 4 5
12 14 15 37 28 table(data$SRP_26n)
2 3 4 5
6 25 49 26 data$SRP26nRev = data$SRP_26n
data$SRP26nRev = ifelse(data$SRP_26n == 1, 5, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 2, 4, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 4, 2, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 5, 1, data$SRP26nRev)
table(data$SRP26nRev)
1 2 3 4
26 49 25 6 table(data$SRP_44n)
1 2 3 4 5
1 6 20 53 26 data$SRP44nRev = data$SRP_44n
data$SRP44nRev = ifelse(data$SRP_44n == 1, 5, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 2, 4, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 4, 2, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 5, 1, data$SRP44nRev)
table(data$SRP44nRev)
1 2 3 4 5
26 53 20 6 1 # ELS
table(data$SRP_14n)
1 2 3 4 5
8 13 17 40 27 data$SRP14nRev = data$SRP_14n
data$SRP14nRev = ifelse(data$SRP_14n == 1, 5, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 2, 4, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 4, 2, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 5, 1, data$SRP14nRev)
table(data$SRP14nRev)
1 2 3 4 5
27 40 17 13 8 table(data$SRP_22n)
1 2 3 4 5
5 29 15 35 22 data$SRP22nRev = data$SRP_22n
data$SRP22nRev = ifelse(data$SRP_22n == 1, 5, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 2, 4, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 4, 2, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 5, 1, data$SRP22nRev)
table(data$SRP22nRev)
1 2 3 4 5
22 35 15 29 5 table(data$SRP_25n)
1 2 3 4 5
9 35 37 16 9 data$SRP25nRev = data$SRP_25n
data$SRP25nRev = ifelse(data$SRP_25n == 1, 5, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 2, 4, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 4, 2, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 5, 1, data$SRP25nRev)
table(data$SRP25nRev)
1 2 3 4 5
9 16 37 35 9 table(data$SRP_36n)
1 2 3 4 5
8 21 15 25 37 data$SRP36nRev = data$SRP_36n
data$SRP36nRev = ifelse(data$SRP_36n == 1, 5, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 2, 4, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 4, 2, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 5, 1, data$SRP36nRev)
table(data$SRP36nRev)
1 2 3 4 5
37 25 15 21 8 table(data$SRP_47n)
1 2 3 4 5
8 54 26 13 5 data$SRP47nRev = data$SRP_47n
data$SRP47nRev = ifelse(data$SRP_47n == 1, 5, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 2, 4, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 4, 2, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 5, 1, data$SRP47nRev)
table(data$SRP47nRev)
1 2 3 4 5
5 13 26 54 8 # ASB
table(data$SRP_05n)
1 2 3 4 5
15 8 2 16 65 data$SRP5nRev = data$SRP_05n
data$SRP5nRev = ifelse(data$SRP_05n == 1, 5, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 2, 4, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 4, 2, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 5, 1, data$SRP5nRev)
table(data$SRP5nRev)
1 2 3 4 5
65 16 2 8 15 table(data$SRP_06n)
1 2 4 5
10 4 17 75 data$SRP6nRev = data$SRP_06n
data$SRP6nRev = ifelse(data$SRP_06n == 1, 5, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 2, 4, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 4, 2, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 5, 1, data$SRP6nRev)
table(data$SRP6nRev)
1 2 4 5
75 17 4 10 table(data$SRP_18n)
1 2 4 5
6 1 12 87 data$SRP18nRev = data$SRP_18n
data$SRP18nRev = ifelse(data$SRP_18n == 1, 5, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 2, 4, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 4, 2, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 5, 1, data$SRP18nRev)
table(data$SRP18nRev)
1 2 4 5
87 12 1 6 table(data$SRP_21n)
1 2 3 4 5
6 13 8 24 55 data$SRP21nRev = data$SRP_21n
data$SRP21nRev = ifelse(data$SRP_21n == 1, 5, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 2, 4, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 4, 2, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 5, 1, data$SRP21nRev)
table(data$SRP21nRev)
1 2 3 4 5
55 24 8 13 6 table(data$SRP_34n)
1 2 3 4 5
3 4 1 13 85 data$SRP34nRev = data$SRP_34n
data$SRP34nRev = ifelse(data$SRP_34n == 1, 5, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 2, 4, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 4, 2, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 5, 1, data$SRP34nRev)
table(data$SRP34nRev)
1 2 3 4 5
85 13 1 4 3 table(data$SRP_46n)
1 2 3 4 5
18 21 2 16 49 data$SRP46nRev = data$SRP_46n
data$SRP46nRev = ifelse(data$SRP_46n == 1, 5, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 2, 4, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 4, 2, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 5, 1, data$SRP46nRev)
table(data$SRP46nRev)
1 2 3 4 5
49 16 2 21 18 Levenson Reverse Codes
(3, 7, 10, 13, 15, 21, 26)
Bold missing from half of surveys
See code book to match the questions in figure to the numeric values in survey.
Citation: Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology, 68(1), 151–158.
table(data$Lev_10n)
1 2 3 4
5 23 52 26 data$Lev_10nRev = data$Lev_10n
data$Lev_10nRev = ifelse(data$Lev_10n == 1, 4, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 2, 3, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 3, 2, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 4, 1, data$Lev_10nRev)
table(data$Lev_10nRev)
1 2 3 4
26 52 23 5 table(data$Lev_13n)
1 2 3 4
3 9 41 53 data$Lev_13nRev = data$Lev_12n
data$Lev_13nRev = ifelse(data$Lev_13n == 1, 4, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 2, 3, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 3, 2, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 4, 1, data$Lev_13nRev)
table(data$Lev_13nRev)
1 2 3 4
53 41 9 3 table(data$Lev_15n)
1 2 3 4
3 8 32 17 data$Lev_15nRev = data$Lev_15n
data$Lev_15nRev = ifelse(data$Lev_15n == 1, 4, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 2, 3, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 3, 2, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 4, 1, data$Lev_15nRev)
table(data$Lev_15nRev)
1 2 3 4
17 32 8 3 table(data$Lev_21n)
1 2 3 4
1 6 46 53 data$Lev_21nRev = data$Lev_21n
data$Lev_21nRev = ifelse(data$Lev_21n == 1, 4, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 2, 3, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 3, 2, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 4, 1, data$Lev_21nRev)
table(data$Lev_21nRev)
1 2 3 4
53 46 6 1 table(data$Lev_26n)
1 2 3 4
6 3 54 43 data$Lev_26nRev = data$Lev_26n
data$Lev_26nRev = ifelse(data$Lev_26n == 1, 4, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 2, 3, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 3, 2, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 4, 1, data$Lev_26nRev)
table(data$Lev_26nRev)
1 2 3 4
43 54 3 6 table(data$Lev_03n)
1 2 3 4
1 12 56 37 data$Lev_03nRev = data$Lev_03n
data$Lev_03nRev = ifelse(data$Lev_03n == 1, 4, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 2, 3, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 3, 2, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 4, 1, data$Lev_03nRev)
table(data$Lev_03nRev)
1 2 3 4
37 56 12 1 table(data$Lev_07n)
1 2 3 4
1 24 56 24 data$Lev_07nRev = data$Lev_07n
data$Lev_07nRev = ifelse(data$Lev_07n == 1, 4, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 2, 3, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 3, 2, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 4, 1, data$Lev_07nRev)
table(data$Lev_07nRev)
1 2 3 4
24 56 24 1 ICU
(1, 3, 5, 8, 13, 14, 15, 16, 17, 19, 23, 24)
Essau, C. A., Sasagawa, S., & Frick, P. J. (2006). Callous-unemotional traits in a community sample of adolescents. Assessment, 13(4), 454-469.
# Callous
table(data$ICU_8n)
2 3 4
16 56 34 data$ICU_8nRev = data$ICU_8n
data$ICU_8nRev = ifelse(data$ICU_8n == 1, 4, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 2, 3, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 3, 2, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 4, 1, data$ICU_8nRev)
table(data$ICU_8nRev)
1 2 3
34 56 16 # Uncaring
table(data$ICU_15n)
1 2 3 4
1 13 41 51 data$ICU_15nRev = data$ICU_15n
data$ICU_15nRev = ifelse(data$ICU_15n == 1, 4, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 2, 3, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 3, 2, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 4, 1, data$ICU_15nRev)
table(data$ICU_15nRev)
1 2 3 4
51 41 13 1 table(data$ICU_23n)
1 2 3 4
1 16 40 49 data$ICU_23nRev = data$ICU_23n
data$ICU_23nRev = ifelse(data$ICU_23n == 1, 4, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 2, 3, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 3, 2, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 4, 1, data$ICU_23nRev)
table(data$ICU_23nRev)
1 2 3 4
49 40 16 1 table(data$ICU_16n)
2 3 4
11 43 52 data$ICU_16nRev = data$ICU_16n
data$ICU_16nRev = ifelse(data$ICU_16n == 1, 4, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 2, 3, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 3, 2, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 4, 1, data$ICU_16nRev)
table(data$ICU_16nRev)
1 2 3
52 43 11 table(data$ICU_3n)
1 2 3 4
1 3 29 71 data$ICU_3nRev = data$ICU_3n
data$ICU_3nRev = ifelse(data$ICU_3n == 1, 4, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 2, 3, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 3, 2, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 4, 1, data$ICU_3nRev)
table(data$ICU_3nRev)
1 2 3 4
71 29 3 1 table(data$ICU_17n)
2 3 4
6 45 55 data$ICU_17nRev = data$ICU_17n
data$ICU_17nRev = ifelse(data$ICU_17n == 1, 4, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 2, 3, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 3, 2, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 4, 1, data$ICU_17nRev)
table(data$ICU_17nRev)
1 2 3
55 45 6 table(data$ICU_24n)
1 2 3 4
6 25 44 31 data$ICU_24nRev = data$ICU_24n
data$ICU_24nRev = ifelse(data$ICU_24n == 1, 4, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 2, 3, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 3, 2, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 4, 1, data$ICU_24nRev)
table(data$ICU_24nRev)
1 2 3 4
31 44 25 6 table(data$ICU_13n)
1 2 3 4
7 46 43 10 data$ICU_13nRev = data$ICU_13n
data$ICU_13nRev = ifelse(data$ICU_13n == 1, 4, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 2, 3, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 3, 2, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 4, 1, data$ICU_13nRev)
table(data$ICU_13nRev)
1 2 3 4
10 43 46 7 table(data$ICU_5n)
1 2 3 4
4 16 42 44 data$ICU_5nRev = data$ICU_5n
data$ICU_5nRev = ifelse(data$ICU_5n == 1, 4, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 2, 3, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 3, 2, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 4, 1, data$ICU_5nRev)
table(data$ICU_5nRev)
1 2 3 4
44 42 16 4 # Unemotional
table(data$ICU_1n)
1 2 3 4
24 48 23 11 data$ICU_1nRev = data$ICU_1n
data$ICU_1nRev = ifelse(data$ICU_1n == 1, 4, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 2, 3, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 3, 2, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 4, 1, data$ICU_1nRev)
table(data$ICU_1nRev)
1 2 3 4
11 23 48 24 table(data$ICU_19n)
1 2 3 4
29 35 26 16 data$ICU_19nRev = data$ICU_19n
data$ICU_19nRev = ifelse(data$ICU_19n == 1, 4, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 2, 3, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 3, 2, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 4, 1, data$ICU_19nRev)
table(data$ICU_19nRev)
1 2 3 4
16 26 35 29 table(data$ICU_14n)
1 2 3 4
26 47 24 9 data$ICU_14nRev = data$ICU_14n
data$ICU_14nRev = ifelse(data$ICU_14n == 1, 4, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 2, 3, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 3, 2, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 4, 1, data$ICU_14nRev)
table(data$ICU_14nRev)
1 2 3 4
9 24 47 26 SSS
(1, 29, 32, 36, 5, 8, 24, 34, 39, 3, 16, 17, 28, 6, 9, 14, 18, 22)
Recoding was done by creating a “false object” or a place holder since it is a binary scale. Example below.
Diagram of recode
\[ A (OriginalValue) -> C(Placeholder) \] \[ B(OrginalValue) -> A(ReversedValue) \]
\[ C(Placeholder) -> B(ReverseValue) \]
# Disinhibition
table(data$ZSSS_1n)
0 1
22 84 data$ZSSS_1nRevFalse <- data$ZSSS_1n
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 0, 2, data$ZSSS_1nRevFalse)
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 1, 0, data$ZSSS_1nRevFalse)
table(data$ZSSS_1nRevFalse)
0 2
84 22 data$ZSSS_1nRev <- data$ZSSS_1nRevFalse
data$ZSSS_1nRev <- ifelse(data$ZSSS_1nRevFalse == 2, 1, data$ZSSS_1nRev)
table(data$ZSSS_1nRev)
0 1
84 22 table(data$ZSSS_29n)
0 1
19 86 data$ZSSS_29nRevFalse <- data$ZSSS_29n
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 0, 2, data$ZSSS_29nRevFalse)
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 1, 0, data$ZSSS_29nRevFalse)
table(data$ZSSS_29nRevFalse)
0 2
86 19 data$ZSSS_29nRev <- data$ZSSS_29nRevFalse
data$ZSSS_29nRev <- ifelse(data$ZSSS_29nRevFalse == 2, 1, data$ZSSS_29nRev)
table(data$ZSSS_29nRev)
0 1
86 19 table(data$ZSSS_32n)
0 1
65 41 data$ZSSS_32nRevFalse <- data$ZSSS_32n
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 0, 2, data$ZSSS_32nRevFalse)
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 1, 0, data$ZSSS_32nRevFalse)
table(data$ZSSS_32nRevFalse)
0 2
41 65 data$ZSSS_32nRev <- data$ZSSS_32nRevFalse
data$ZSSS_32nRev <- ifelse(data$ZSSS_32nRevFalse == 2, 1, data$ZSSS_32nRev)
table(data$ZSSS_32nRev)
0 1
41 65 table(data$ZSSS_36n)
0 1
41 63 data$ZSSS_36nRevFalse <- data$ZSSS_36n
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 0, 2, data$ZSSS_36nRevFalse)
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 1, 0, data$ZSSS_36nRevFalse)
table(data$ZSSS_36nRevFalse)
0 2
63 41 data$ZSSS_36nRev <- data$ZSSS_36nRevFalse
data$ZSSS_36nRev <- ifelse(data$ZSSS_36nRevFalse == 2, 1, data$ZSSS_36nRev)
table(data$ZSSS_36nRev)
0 1
63 41 # Boredom
table(data$ZSSS_5n)
0 1
12 94 data$ZSSS_5nRevFalse <- data$ZSSS_5n
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 0, 2, data$ZSSS_5nRevFalse)
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 1, 0, data$ZSSS_5nRevFalse)
table(data$ZSSS_5nRevFalse)
0 2
94 12 data$ZSSS_5nRev <- data$ZSSS_5nRevFalse
data$ZSSS_5nRev <- ifelse(data$ZSSS_5nRevFalse == 2, 1, data$ZSSS_5nRev)
table(data$ZSSS_5nRev)
0 1
94 12 table(data$ZSSS_8n)
0 1
31 75 data$ZSSS_8nRevFalse <- data$ZSSS_8n
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 0, 2, data$ZSSS_8nRevFalse)
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 1, 0, data$ZSSS_8nRevFalse)
table(data$ZSSS_8nRevFalse)
0 2
75 31 data$ZSSS_8nRev <- data$ZSSS_8nRevFalse
data$ZSSS_8nRev <- ifelse(data$ZSSS_8nRevFalse == 2, 1, data$ZSSS_8nRev)
table(data$ZSSS_8nRev)
0 1
75 31 table(data$ZSSS_24n)
0 1
24 82 data$ZSSS_24nRevFalse <- data$ZSSS_24n
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 0, 2, data$ZSSS_24nRevFalse)
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 1, 0, data$ZSSS_24nRevFalse)
table(data$ZSSS_24nRevFalse)
0 2
82 24 data$ZSSS_24nRev <- data$ZSSS_24nRevFalse
data$ZSSS_24nRev <- ifelse(data$ZSSS_24nRevFalse == 2, 1, data$ZSSS_24nRev)
table(data$ZSSS_24nRev)
0 1
82 24 table(data$ZSSS_34n)
0 1
33 73 data$ZSSS_34nRevFalse <- data$ZSSS_34n
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 0, 2, data$ZSSS_34nRevFalse)
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 1, 0, data$ZSSS_34nRevFalse)
table(data$ZSSS_34nRevFalse)
0 2
73 33 data$ZSSS_34nRev <- data$ZSSS_34nRevFalse
data$ZSSS_34nRev <- ifelse(data$ZSSS_34nRevFalse == 2, 1, data$ZSSS_34nRev)
table(data$ZSSS_34nRev)
0 1
73 33 table(data$ZSSS_39n)
0 1
26 80 data$ZSSS_39nRevFalse <- data$ZSSS_39n
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 0, 2, data$ZSSS_39nRevFalse)
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 1, 0, data$ZSSS_39nRevFalse)
table(data$ZSSS_39nRevFalse)
0 2
80 26 data$ZSSS_39nRev <- data$ZSSS_39nRevFalse
data$ZSSS_39nRev <- ifelse(data$ZSSS_39nRevFalse == 2, 1, data$ZSSS_39nRev)
table(data$ZSSS_39nRev)
0 1
80 26 # Thrill
table(data$ZSSS_3n)
0 1
61 45 data$ZSSS_3nRevFalse <- data$ZSSS_3n
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 0, 2, data$ZSSS_3nRevFalse)
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 1, 0, data$ZSSS_3nRevFalse)
table(data$ZSSS_3nRevFalse)
0 2
45 61 data$ZSSS_3nRev <- data$ZSSS_3nRevFalse
data$ZSSS_3nRev <- ifelse(data$ZSSS_3nRevFalse == 2, 1, data$ZSSS_3nRev)
table(data$ZSSS_3nRev)
0 1
45 61 table(data$ZSSS_16n)
0 1
67 39 data$ZSSS_16nRevFalse <- data$ZSSS_16n
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 0, 2, data$ZSSS_16nRevFalse)
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 1, 0, data$ZSSS_16nRevFalse)
table(data$ZSSS_16nRevFalse)
0 2
39 67 data$ZSSS_16nRev <- data$ZSSS_16nRevFalse
data$ZSSS_16nRev <- ifelse(data$ZSSS_16nRevFalse == 2, 1, data$ZSSS_16nRev)
table(data$ZSSS_16nRev)
0 1
39 67 table(data$ZSSS_17n)
0 1
80 26 data$ZSSS_17nRevFalse <- data$ZSSS_17n
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 0, 2, data$ZSSS_17nRevFalse)
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 1, 0, data$ZSSS_17nRevFalse)
table(data$ZSSS_17nRevFalse)
0 2
26 80 data$ZSSS_17nRev <- data$ZSSS_17nRevFalse
data$ZSSS_17nRev <- ifelse(data$ZSSS_17nRevFalse == 2, 1, data$ZSSS_17nRev)
table(data$ZSSS_17nRev)
0 1
26 80 table(data$ZSSS_23n)
0 1
70 36 data$ZSSS_23nRevFalse <- data$ZSSS_23n
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 0, 2, data$ZSSS_23nRevFalse)
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 1, 0, data$ZSSS_23nRevFalse)
table(data$ZSSS_23nRevFalse)
0 2
36 70 data$ZSSS_23nRev <- data$ZSSS_23nRevFalse
data$ZSSS_23nRev <- ifelse(data$ZSSS_23nRevFalse == 2, 1, data$ZSSS_23nRev)
table(data$ZSSS_23nRev)
0 1
36 70 table(data$ZSSS_28n)
0 1
45 60 data$ZSSS_28nRevFalse <- data$ZSSS_28n
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 0, 2, data$ZSSS_28nRevFalse)
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 1, 0, data$ZSSS_28nRevFalse)
table(data$ZSSS_28nRevFalse)
0 2
60 45 data$ZSSS_28nRev <- data$ZSSS_28nRevFalse
data$ZSSS_28nRev <- ifelse(data$ZSSS_28nRevFalse == 2, 1, data$ZSSS_28nRev)
table(data$ZSSS_28nRev)
0 1
60 45 # Exp
table(data$ZSSS_6n)
0 1
61 45 data$ZSSS_6nRevFalse <- data$ZSSS_6n
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 0, 2, data$ZSSS_6nRevFalse)
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 1, 0, data$ZSSS_6nRevFalse)
table(data$ZSSS_6nRevFalse)
0 2
45 61 data$ZSSS_6nRev <- data$ZSSS_6nRevFalse
data$ZSSS_6nRev <- ifelse(data$ZSSS_6nRevFalse == 2, 1, data$ZSSS_6nRev)
table(data$ZSSS_6nRev)
0 1
45 61 table(data$ZSSS_9n)
0 1
61 45 data$ZSSS_9nRevFalse <- data$ZSSS_9n
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 0, 2, data$ZSSS_9nRevFalse)
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 1, 0, data$ZSSS_9nRevFalse)
table(data$ZSSS_9nRevFalse)
0 2
45 61 data$ZSSS_9nRev <- data$ZSSS_9nRevFalse
data$ZSSS_9nRev <- ifelse(data$ZSSS_9nRevFalse == 2, 1, data$ZSSS_9nRev)
table(data$ZSSS_9nRev)
0 1
45 61 table(data$ZSSS_14n)
0 1
59 47 data$ZSSS_14nRevFalse <- data$ZSSS_14n
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 0, 2, data$ZSSS_14nRevFalse)
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 1, 0, data$ZSSS_14nRevFalse)
table(data$ZSSS_14nRevFalse)
0 2
47 59 data$ZSSS_14nRev <- data$ZSSS_14nRevFalse
data$ZSSS_14nRev <- ifelse(data$ZSSS_14nRevFalse == 2, 1, data$ZSSS_14nRev)
table(data$ZSSS_14nRev)
0 1
47 59 table(data$ZSSS_18n)
0 1
51 55 data$ZSSS_18nRevFalse <- data$ZSSS_18n
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 0, 2, data$ZSSS_18nRevFalse)
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 1, 0, data$ZSSS_18nRevFalse)
table(data$ZSSS_18nRevFalse)
0 2
55 51 data$ZSSS_18nRev <- data$ZSSS_18nRevFalse
data$ZSSS_18nRev <- ifelse(data$ZSSS_18nRevFalse == 2, 1, data$ZSSS_18nRev)
table(data$ZSSS_18nRev)
0 1
55 51 table(data$ZSSS_22n)
0 1
91 14 data$ZSSS_22nRevFalse <- data$ZSSS_22n
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 0, 2, data$ZSSS_22nRevFalse)
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 1, 0, data$ZSSS_22nRevFalse)
table(data$ZSSS_22nRevFalse)
0 2
14 91 data$ZSSS_22nRev <- data$ZSSS_22nRevFalse
data$ZSSS_22nRev <- ifelse(data$ZSSS_22nRevFalse == 2, 1, data$ZSSS_22nRev)
table(data$ZSSS_22nRev)
0 1
14 91 Scales
SRP
# SRP Tot
data$SRPTotalScore <- (data$SRP_01n + data$SRP_02n + data$SRP_03n + data$SRP_04n + data$SRP5nRev + data$SRP6nRev + data$SRP_07n +
data$SRP_08n + data$SRP_09n + data$SRP_10n + data$SRP11nRev + data$SRP_12n + data$SRP_13n + data$SRP14nRev +
data$SRP_15n + data$SRP16nRev + data$SRP_17n + data$SRP18nRev + data$SRP19nRev + data$SRP_20n + data$SRP21nRev +
data$SRP22nRev + data$SRP23nRev + data$SRP24nRev + data$SRP25nRev + data$SRP26nRev + data$SRP_27n + data$SRP_28n +
data$SRP_29n + data$SRP_30n + data$SRP31nRev + data$SRP_32n + data$SRP_33n + data$SRP34nRev + data$SRP_35n +
data$SRP36nRev + data$SRP_37n + data$SRP38nRev + data$SRP_39n + data$SRP_40n + data$SRP_41n + data$SRP_42n +
data$SRP_43n + data$SRP44nRev + data$SRP_45n + data$SRP46nRev + data$SRP47nRev + data$SRP_48n + data$SRP_49n +
data$SRP_50n + data$SRP_51n + data$SRP_52n + data$SRP_53n + data$SRP_54n + data$SRP_55n + data$SRP_56n +
data$SRP_57n + data$SRP_58n + data$SRP_59n + data$SRP_60n + data$SRP61nRev + data$SRP_62n + data$SRP_63n + data$SRP_64n)
#SRP IPM
data$SRPIPMTotal <- (data$SRP_03n + data$SRP_08n + data$SRP_13n + data$SRP16nRev + data$SRP_20n + data$SRP24nRev + data$SRP_27n + data$SRP31nRev +
data$SRP_35n + data$SRP38nRev + data$SRP_41n + data$SRP_45n + data$SRP_50n + data$SRP_54n + data$SRP_58n + data$SRP61nRev)
# SRP Callous
data$SRPCATotal <- (data$SRP_02n + data$SRP_07n + data$SRP11nRev + data$SRP_15n + data$SRP19nRev + data$SRP23nRev + data$SRP26nRev + data$SRP_30n + data$SRP_33n + data$SRP_37n + data$SRP_40n + data$SRP44nRev + data$SRP_48n + data$SRP_53n + data$SRP_56n + data$SRP_60n)
#SRP lifestyle
data$SRPELSTotal <- (data$SRP_01n + data$SRP_04n + data$SRP_09n + data$SRP14nRev + data$SRP_17n + data$SRP22nRev + data$SRP25nRev + data$SRP_28n + data$SRP_32n + data$SRP36nRev + data$SRP_39n + data$SRP_42n + data$SRP47nRev + data$SRP_51n +data$SRP_55n + data$SRP_59n)
# SRP Antisocial
data$SRPASBTotal <- (data$SRP5nRev + data$SRP6nRev + data$SRP_10n + data$SRP_12n + data$SRP18nRev + data$SRP21nRev + data$SRP_29n + data$SRP34nRev + data$SRP_43n + data$SRP46nRev + data$SRP_49n + data$SRP_52n + data$SRP_57n + data$SRP_62n + data$SRP_63n + data$SRP_64n)ICU
# ICU total
data$ICUTotScore <- (data$ICU_1nRev + data$ICU_2n + data$ICU_3nRev + data$ICU_4n + data$ICU_5nRev + data$ICU_6n +
data$ICU_7n + data$ICU_8nRev + data$ICU_9n + data$ICU_10n + data$ICU_11n + data$ICU_12n + data$ICU_13nRev +
data$ICU_14nRev + data$ICU_15nRev + data$ICU_16nRev + data$ICU_17nRev + data$ICU_18n + data$ICU_19nRev +
data$ICU_20n + data$ICU_21n + data$ICU_22n + data$ICU_23nRev + data$ICU_24nRev)
# ICU Cal
data$ICUCalTotalScore <- (data$ICU_4n + data$ICU_8nRev + data$ICU_9n + data$ICU_18n + data$ICU_11n + data$ICU_21n + data$ICU_7n + data$ICU_20n +
data$ICU_2n + data$ICU_12n + data$ICU_10n)
# ICU Uncare
data$ICUUncareTotalScore <- (data$ICU_15nRev + data$ICU_23nRev + data$ICU_16nRev + data$ICU_3nRev + data$ICU_17nRev + data$ICU_24nRev +
data$ICU_13nRev + data$ICU_5nRev)
# ICU Unemo
data$ICUUnemoTotal <- (data$ICU_1nRev + data$ICU_19nRev + data$ICU_6n + data$ICU_22n + data$ICU_14nRev)LSRP
# Total
data$LevTotalScore <- (data$Lev_01n + data$Lev_02n + data$Lev_03nRev + data$Lev_04n + data$Lev_05n + data$Lev_06n + data$Lev_07nRev + data$Lev_08n + data$Lev_09n + data$Lev_10nRev + data$Lev_11n + data$Lev_12n + data$Lev_13nRev + data$Lev_16n + data$Lev_17n + data$Lev_18n + data$Lev_19n + data$Lev_20n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)
# Primary
data$LevPrimTotalScore <- (data$Lev_02n + data$Lev_04n + data$Lev_07nRev + data$Lev_09n + data$Lev_11n + data$Lev_12n + data$Lev_13nRev +
data$Lev_17n + data$Lev_19n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)
# Seconnday
data$LevSecTotalScore <- (data$Lev_01n + data$Lev_03nRev + data$Lev_05n + data$Lev_06n + data$Lev_08n + data$Lev_10nRev + data$Lev_16n + data$Lev_18n + data$Lev_20n)ZSSS
# Total
data$SSSTotalScore <- (data$ZSSS_1nRev + data$ZSSS_2n + data$ZSSS_3nRev + data$ZSSS_4n + data$ZSSS_5nRev + data$ZSSS_6nRev + data$ZSSS_7n + data$ZSSS_8nRev + data$ZSSS_9nRev + data$ZSSS_10n + data$ZSSS_11n + data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_14nRev + data$ZSSS_15n + data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_18nRev + data$ZSSS_19n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_22nRev + data$ZSSS_23nRev + data$ZSSS_24nRev + data$ZSSS_25n + data$ZSSS_26n + data$ZSSS_27n + data$ZSSS_28nRev + data$ZSSS_29nRev + data$ZSSS_30n + data$ZSSS_31n + data$ZSSS_32nRev + data$ZSSS_33n + data$ZSSS_34nRev + data$ZSSS_35n + data$ZSSS_36nRev + data$ZSSS_37n + data$ZSSS_38n + data$ZSSS_39nRev + data$ZSSS_40n)
# Disinhibited
data$SSSDISTotal <- (data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_25n + data$ZSSS_30n + data$ZSSS_33n + data$ZSSS_35n +
data$ZSSS_1nRev + data$ZSSS_29nRev + data$ZSSS_32nRev + data$ZSSS_36nRev)
# Boredom
data$SSSBorTotal <- (data$ZSSS_2n + data$ZSSS_7n + data$ZSSS_15n + data$ZSSS_27n + data$ZSSS_31n + data$ZSSS_5nRev + data$ZSSS_8nRev + data$ZSSS_24nRev +
data$ZSSS_34nRev + data$ZSSS_39nRev)
# Thrill
data$SSSThrilTotal <- (data$ZSSS_11n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_38n + data$ZSSS_40n + data$ZSSS_3nRev +
data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_23nRev + data$ZSSS_28nRev)
# Exp
data$SSSExpTotal <- (data$ZSSS_4n + data$ZSSS_10n + data$ZSSS_19n + data$ZSSS_26n + data$ZSSS_37n + data$ZSSS_6nRev +
data$ZSSS_9nRev + data$ZSSS_14nRev + data$ZSSS_18nRev + data$ZSSS_22nRev) Autonomic Measures
We excluded zero values from the analysis during the first 11 seconds and the final 30 seconds, as they likely resulted from slight hand movements by the participant during the phases.
Resting Heart Rate
data$HRbaseline <- (data$HRT_00_11 + data$HRT_00_12 + data$HRT_00_13 + data$HRT_00_14 + data$HRT_00_15 + data$HRT_00_16 + data$HRT_00_17 + data$HRT_00_18 + data$HRT_00_19 + data$HRT_00_20 + data$HRT_00_21 + data$HRT_00_22 + data$HRT_00_23 + data$HRT_00_24 + data$HRT_00_25 + data$HRT_00_26 + data$HRT_00_27 + data$HRT_00_28 + data$HRT_00_29 + data$HRT_00_30 + data$HRT_00_31 + data$HRT_00_32 + data$HRT_00_33 + data$HRT_00_34 + data$HRT_00_35 + data$HRT_00_36 + data$HRT_00_37 + data$HRT_00_38 + data$HRT_00_39 + data$HRT_00_40 + data$HRT_00_41 + data$HRT_00_42 + data$HRT_00_43 + data$HRT_00_44 + data$HRT_00_45 + data$HRT_00_46 + data$HRT_00_47 + data$HRT_00_48 + data$HRT_00_49 + data$HRT_00_50 + data$HRT_00_51 + data$HRT_00_52 + data$HRT_00_53 + data$HRT_00_54 + data$HRT_00_55 + data$HRT_00_56 + data$HRT_00_57 + data$HRT_00_58 + data$HRT_00_59 + data$HRT_01_00 + data$HRT_01_01 + data$HRT_01_02 + data$HRT_01_03 + data$HRT_01_04 + data$HRT_01_05 + data$HRT_01_06 + data$HRT_01_07 + data$HRT_01_08 + data$HRT_01_09 + data$HRT_01_10 + data$HRT_01_11 + data$HRT_01_12 + data$HRT_01_13 + data$HRT_01_14 + data$HRT_01_15 + data$HRT_01_16 + data$HRT_01_17 + data$HRT_01_18 + data$HRT_01_19 + data$HRT_01_20 + data$HRT_01_21 + data$HRT_01_22 + data$HRT_01_23 + data$HRT_01_24 + data$HRT_01_25 + data$HRT_01_26 + data$HRT_01_27 + data$HRT_01_28 + data$HRT_01_29 + data$HRT_01_30 + data$HRT_01_31 + data$HRT_01_32 + data$HRT_01_33 + data$HRT_01_34 + data$HRT_01_35 + data$HRT_01_36 + data$HRT_01_37 + data$HRT_01_38 + data$HRT_01_39 + data$HRT_01_40 + data$HRT_01_41 + data$HRT_01_42 + data$HRT_01_43 + data$HRT_01_44 + data$HRT_01_45 + data$HRT_01_46 + data$HRT_01_47 + data$HRT_01_48 + data$HRT_01_49 + data$HRT_01_50 + data$HRT_01_51 + data$HRT_01_52 + data$HRT_01_53 + data$HRT_01_54 + data$HRT_01_55 + data$HRT_01_56 + data$HRT_01_57 + data$HRT_01_58 + data$HRT_01_59 + data$HRT_02_00 + data$HRT_02_01 + data$HRT_02_02 + data$HRT_02_03 + data$HRT_02_04 + data$HRT_02_05 + data$HRT_02_06 + data$HRT_02_07 + data$HRT_02_08 + data$HRT_02_09 + data$HRT_02_10 + data$HRT_02_11 + data$HRT_02_12 + data$HRT_02_13 + data$HRT_02_14 + data$HRT_02_15 + data$HRT_02_16 + data$HRT_02_17 + data$HRT_02_18 + data$HRT_02_19 + data$HRT_02_20 + data$HRT_02_21 + data$HRT_02_22 + data$HRT_02_23 + data$HRT_02_24 + data$HRT_02_25 + data$HRT_02_26 + data$HRT_02_27 + data$HRT_02_28 + data$HRT_02_29)/140Resting Skin Conductance
data$SCbaseline <- (data$SCT_00_11 + data$SCT_00_12 + data$SCT_00_13 + data$SCT_00_14 + data$SCT_00_15 + data$SCT_00_16 + data$SCT_00_17 + data$SCT_00_18 + data$SCT_00_19 + data$SCT_00_20 + data$SCT_00_21 + data$SCT_00_22 + data$SCT_00_23 + data$SCT_00_24 + data$SCT_00_25 + data$SCT_00_26 + data$SCT_00_27 + data$SCT_00_28 + data$SCT_00_29 + data$SCT_00_30 + data$SCT_00_31 + data$SCT_00_32 + data$SCT_00_33 + data$SCT_00_34 + data$SCT_00_35 + data$SCT_00_36 + data$SCT_00_37 + data$SCT_00_38 + data$SCT_00_39 + data$SCT_00_40 + data$SCT_00_41 + data$SCT_00_42 + data$SCT_00_43 + data$SCT_00_44 + data$SCT_00_45 + data$SCT_00_46 + data$SCT_00_47 + data$SCT_00_48 + data$SCT_00_49 + data$SCT_00_50 + data$SCT_00_51 + data$SCT_00_52 + data$SCT_00_53 + data$SCT_00_54 + data$SCT_00_55 + data$SCT_00_56 + data$SCT_00_57 + data$SCT_00_58 + data$SCT_00_59 + data$SCT_01_00 + data$SCT_01_01 + data$SCT_01_02 + data$SCT_01_03 + data$SCT_01_04 + data$SCT_01_05 + data$SCT_01_06 + data$SCT_01_07 + data$SCT_01_08 + data$SCT_01_09 + data$SCT_01_10 + data$SCT_01_11 + data$SCT_01_12 + data$SCT_01_13 + data$SCT_01_14 + data$SCT_01_15 + data$SCT_01_16 + data$SCT_01_17 + data$SCT_01_18 + data$SCT_01_19 + data$SCT_01_20 + data$SCT_01_21 + data$SCT_01_22 + data$SCT_01_23 + data$SCT_01_24 + data$SCT_01_25 + data$SCT_01_26 + data$SCT_01_27 + data$SCT_01_28 + data$SCT_01_29 + data$SCT_01_30 + data$SCT_01_31 + data$SCT_01_32 + data$SCT_01_33 + data$SCT_01_34 + data$SCT_01_35 + data$SCT_01_36 + data$SCT_01_37 + data$SCT_01_38 + data$SCT_01_39 + data$SCT_01_40 + data$SCT_01_41 + data$SCT_01_42 + data$SCT_01_43 + data$SCT_01_44 + data$SCT_01_45 + data$SCT_01_46 + data$SCT_01_47 + data$SCT_01_48 + data$SCT_01_49 + data$SCT_01_50 + data$SCT_01_51 + data$SCT_01_52 + data$SCT_01_53 + data$SCT_01_54 + data$SCT_01_55 + data$SCT_01_56 + data$SCT_01_57 + data$SCT_01_58 + data$SCT_01_59 + data$SCT_02_00 + data$SCT_02_01 + data$SCT_02_02 + data$SCT_02_03 + data$SCT_02_04 + data$SCT_02_05 + data$SCT_02_06 + data$SCT_02_07 + data$SCT_02_08 + data$SCT_02_09 + data$SCT_02_10 + data$SCT_02_11 + data$SCT_02_12 + data$SCT_02_13 + data$SCT_02_14 + data$SCT_02_15 + data$SCT_02_16 + data$SCT_02_17 + data$SCT_02_18 + data$SCT_02_19 + data$SCT_02_20 + data$SCT_02_21 + data$SCT_02_22 + data$SCT_02_23 + data$SCT_02_24 + data$SCT_02_25 + data$SCT_02_26 + data$SCT_02_27 + data$SCT_02_28 + data$SCT_02_29)/140The formula for the AUC code below can be found here.
Difference Scores Countdown
This was calculated using the following:
\(Change Score = T2 (12 \,second \,countdown \,phase) - T1 (last \,5 \,seconds \, of \,rest \, phase)/trial time\)
SC
# Signaled
data$CDSkin5secT1 <- (data$ScStr_00_07 + data$ScStr_00_08 + data$ScStr_00_09 + data$ScStr_00_10 + data$ScStr_00_11)/5
data$CDSkin12secT1 <- (data$ScStr_00_12 + data$ScStr_00_13 + data$ScStr_00_14 + data$ScStr_00_15 + data$ScStr_00_16 + data$ScStr_00_17 +
data$ScStr_00_18 + data$ScStr_00_19 + data$ScStr_00_20 + data$ScStr_00_21 + data$ScStr_00_22 + data$ScStr_00_23)/12
data$CDSkinDifT1 <- data$CDSkin12secT1 - data$CDSkin5secT1
data$CDSkin5secT3 <- (data$ScStr_01_37 + data$ScStr_01_38 + data$ScStr_01_39 + data$ScStr_01_40 + data$ScStr_01_41)/5
data$CDSkin12secT3 <- (data$ScStr_01_42 + data$ScStr_01_43 + data$ScStr_01_44 + data$ScStr_01_45 + data$ScStr_01_46 + data$ScStr_01_47 +
data$ScStr_01_48 + data$ScStr_01_49 + data$ScStr_01_50 + data$ScStr_01_51 + data$ScStr_01_52 + data$ScStr_01_53)/12
data$CDSkinDifT3 <- data$CDSkin12secT3 - data$CDSkin5secT3
# Signaled mean
data$CDSkinSignal12 <- (data$CDSkinDifT1 + data$CDSkinDifT3)/2
# Unsignaled
data$CDSkin5secT2 <- (data$ScStr_00_52 + data$ScStr_00_53 + data$ScStr_00_54 + data$ScStr_00_55 + data$ScStr_00_56)/5
data$CDSkin12secT2 <- (data$ScStr_00_57 + data$ScStr_00_58 + data$ScStr_00_59 + data$ScStr_01_00 + data$ScStr_01_01 + data$ScStr_01_02 + data$ScStr_01_03 + data$ScStr_01_04 + data$ScStr_01_05 + data$ScStr_01_06 + data$ScStr_01_07 + data$ScStr_01_08)/12
data$CDSkinDifT2 <- data$CDSkin12secT2 - data$CDSkin5secT2
data$CDSkin5secT4 <- (data$ScStr_02_22 + data$ScStr_02_23 + data$ScStr_02_24 + data$ScStr_02_25 + data$ScStr_02_26)/5
data$CDSkin12secT4 <- (data$ScStr_02_27 + data$ScStr_02_28 + data$ScStr_02_29 + data$ScStr_02_30 + data$ScStr_02_31 + data$ScStr_02_32 +
data$ScStr_02_33 + data$ScStr_02_34 + data$ScStr_02_35 + data$ScStr_02_36 + data$ScStr_02_37 + data$ScStr_02_38)/12
data$CDSkinDifT4 <- data$CDSkin12secT4 - data$CDSkin5secT4
# Unsignaled mean
data$CDSkinUnsig12 <- (data$CDSkinDifT2 + data$CDSkinDifT4)/2HR
# Signaled
data$CDHeart5secT1 <- (data$HrStr_00_07 + data$HrStr_00_08 + data$HrStr_00_09 + data$HrStr_00_10 + data$HrStr_00_11)/5
data$CDHeart12secT1 <- (data$HrStr_00_12 + data$HrStr_00_13 + data$HrStr_00_14 + data$HrStr_00_15 + data$HrStr_00_16 + data$HrStr_00_17 + data$HrStr_00_18 + data$HrStr_00_19 + data$HrStr_00_20 + data$HrStr_00_21 + data$HrStr_00_22 + data$HrStr_00_23)/12
data$CDHeartDifT1 <- data$CDHeart12secT1 - data$CDHeart5secT1
data$CDHeart5secT3 <- (data$HrStr_01_37 + data$HrStr_01_38 + data$HrStr_01_39 + data$HrStr_01_40 + data$HrStr_01_41)/5
data$CDHeart12secT3 <- (data$HrStr_01_42 + data$HrStr_01_43 + data$HrStr_01_44 + data$HrStr_01_45 + data$HrStr_01_46 + data$HrStr_01_47 + data$HrStr_01_48 + data$HrStr_01_49 + data$HrStr_01_50 + data$HrStr_01_51 + data$HrStr_01_52 + data$HrStr_01_53)/12
data$CDHeartDifT3 <- data$CDHeart12secT3 - data$CDHeart5secT3
data$CDHeartSignal12 <- (data$CDHeartDifT1 + data$CDHeartDifT3)/2
# Unsignaled
data$CDHeart5secT2 <- (data$HrStr_00_52 + data$HrStr_00_53 + data$HrStr_00_54 + data$HrStr_00_55 + data$HrStr_00_56)/5
data$CDHeart12secT2 <- (data$HrStr_00_57 + data$HrStr_00_58 + data$HrStr_00_59 + data$HrStr_01_00 + data$HrStr_01_01 + data$HrStr_01_02 + data$HrStr_01_03 + data$HrStr_01_04 + data$HrStr_01_05 + data$HrStr_01_06 + data$HrStr_01_07 + data$HrStr_01_08)/12
data$CDHeartDifT2 <- data$CDHeart12secT2 - data$CDHeart5secT2
data$CDHeart5secT4 <- (data$HrStr_02_22 + data$HrStr_02_23 + data$HrStr_02_24 + data$HrStr_02_25 + data$HrStr_02_26)/5
data$CDHeart12secT4 <- (data$HrStr_02_27 + data$HrStr_02_28 + data$HrStr_02_29 + data$HrStr_02_30 + data$HrStr_02_31 + data$HrStr_02_32 + data$HrStr_02_33 + data$HrStr_02_34 + data$HrStr_02_35 + data$HrStr_02_36 + data$HrStr_02_37 + data$HrStr_02_38)/12
data$CDHeartDifT4 <- data$CDHeart12secT4 - data$CDHeart5secT4
# Unsignaled mean
data$CDHeartUnsig12 <- (data$CDHeartDifT2 + data$CDHeartDifT4)/2Countdown Recovery Measures
This was calculated using the following:
\(Recovery Score = (mean \, of \, the \, 12 \, seconds \, after \, noise blast \, – 5 \, mean \, of \, the \,seconds \, before \, the \,noise blast)\)
Heart Rate Change Recovery (HRCR)
# Trial 1
data$HrT112Rec <- (data$HrStr_00_25 + data$HrStr_00_26 + data$HrStr_00_27 + data$HrStr_00_28 + data$HrStr_00_29 + data$HrStr_00_30 + data$HrStr_00_31 + data$HrStr_00_32 + data$HrStr_00_33 + data$HrStr_00_34 + data$HrStr_00_35 + data$HrStr_00_36)/12
data$HrT15Rec <- (data$HrStr_00_19 + data$HrStr_00_20 + data$HrStr_00_21 + data$HrStr_00_22 + data$HrStr_00_23)/5
data$HrT1Recov <- (data$HrT112Rec - data$HrT15Rec)
# Trial 2
data$HrT212Rec <- (data$HrStr_01_10 + data$HrStr_01_11 + data$HrStr_01_12 + data$HrStr_01_13 + data$HrStr_01_14 + data$HrStr_01_15 + data$HrStr_01_16 + data$HrStr_01_17 + data$HrStr_01_18 + data$HrStr_01_19 + data$HrStr_01_20 + data$HrStr_01_21)/12
data$HrT25Rec <- (data$HrStr_01_04 + data$HrStr_01_05 + data$HrStr_01_06 + data$HrStr_01_07 + data$HrStr_01_08)/5
data$HrT2Recov <- (data$HrT212Rec - data$HrT25Rec)
# Trial 3
data$HrT312Rec <- (data$HrStr_01_55 + data$HrStr_01_56 + data$HrStr_01_57 + data$HrStr_01_58 + data$HrStr_01_59 + data$HrStr_02_00 + data$HrStr_02_01 + data$HrStr_02_02 + data$HrStr_02_03 + data$HrStr_02_04 + data$HrStr_02_05 + data$HrStr_02_06)/12
data$HrT35Rec <- (data$HrStr_01_49 + data$HrStr_01_50 + data$HrStr_01_51 + data$HrStr_01_52 + data$HrStr_01_53)/5
data$HrT3Recov <- (data$HrT312Rec - data$HrT35Rec)
# Trial 4
data$HrT412Rec <- (data$HrStr_02_40 + data$HrStr_02_41 + data$HrStr_02_42 + data$HrStr_02_43 + data$HrStr_02_44 + data$HrStr_02_45 + data$HrStr_02_46 + data$HrStr_02_47 + data$HrStr_02_48 + data$HrStr_02_49 + data$HrStr_02_50 + data$HrStr_02_51)/12
data$HrT45Rec <- (data$HrStr_02_34 + data$HrStr_02_35 + data$HrStr_02_36 + data$HrStr_02_37 + data$HrStr_02_38)/5
data$HrT4Recov <- (data$HrT412Rec - data$HrT45Rec)
# Signaled
data$HrSigRecovMean <- (data$HrT1Recov + data$HrT3Recov)/2
# Unsignaled
data$HrUnSigRecovMean <- (data$HrT2Recov + data$HrT4Recov)/2Skin Conductance Level Change Recover (SCLCR)
# Trial 1
data$ScT112Rec <- (data$ScStr_00_25 + data$ScStr_00_26 + data$ScStr_00_27 + data$ScStr_00_28 + data$ScStr_00_29 + data$ScStr_00_30 + data$ScStr_00_31 + data$ScStr_00_32 + data$ScStr_00_33 + data$ScStr_00_34 + data$ScStr_00_35 + data$ScStr_00_36)/12
data$ScT15Rec <- (data$ScStr_00_19 + data$ScStr_00_20 + data$ScStr_00_21 + data$ScStr_00_22 + data$ScStr_00_23)/5
data$ScT1Recov <- (data$ScT112Rec - data$ScT15Rec)
# Trial 2
data$ScT212Rec <- (data$ScStr_01_10 + data$ScStr_01_11 + data$ScStr_01_12 + data$ScStr_01_13 + data$ScStr_01_14 + data$ScStr_01_15 + data$ScStr_01_16 + data$ScStr_01_17 + data$ScStr_01_18 + data$ScStr_01_19 + data$ScStr_01_20 + data$ScStr_01_21)/12
data$ScT25Rec <- (data$ScStr_01_04 + data$ScStr_01_05 + data$ScStr_01_06 + data$ScStr_01_07 + data$ScStr_01_08)/5
data$ScT2Recov <- (data$ScT212Rec - data$ScT25Rec)
# Trial 3
data$ScT312Rec <- (data$ScStr_01_55 + data$ScStr_01_56 + data$ScStr_01_57 + data$ScStr_01_58 + data$ScStr_01_59 + data$ScStr_02_00 + data$ScStr_02_01 + data$ScStr_02_02 + data$ScStr_02_03 + data$ScStr_02_04 + data$ScStr_02_05 + data$ScStr_02_06)/12
data$ScT35Rec <- (data$ScStr_01_49 + data$ScStr_01_50 + data$ScStr_01_51 + data$ScStr_01_52 + data$ScStr_01_53)/5
data$ScT3Recov <- (data$ScT312Rec - data$ScT35Rec)
# Trial 4
data$ScT412Rec <- (data$ScStr_02_40 + data$ScStr_02_41 + data$ScStr_02_42 + data$ScStr_02_43 + data$ScStr_02_44 + data$ScStr_02_45 + data$ScStr_02_46 + data$ScStr_02_47 + data$ScStr_02_48 + data$ScStr_02_49 + data$ScStr_02_50 + data$ScStr_02_51)/12
data$ScT45Rec <- (data$ScStr_02_34 + data$ScStr_02_35 + data$ScStr_02_36 + data$ScStr_02_37 + data$ScStr_02_38)/5
data$ScT4Recov <- (data$ScT412Rec - data$ScT45Rec)
# Signaled
data$ScSigRecovMean <- (data$ScT1Recov + data$ScT3Recov)/2
# Unsignaled
data$ScUnSigRecovMean <- (data$ScT2Recov + data$ScT4Recov)/2Wrangling
Full Sample
Table 1
#Full
FullsampleFinalSurveyT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal)
FSFSurveyT1 <- FullsampleFinalSurveyT1 |>
na.omit()
FullsampleFinalHRT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline)
FSFHRT1 <- FullsampleFinalHRT1 |>
na.omit()
FullsampleFinalSCT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline)
FSFSCT1 <- FullsampleFinalSCT1 |>
na.omit()
# Social Stressor
SocialStressorFinalHRT1 <- data |>
dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi, HRbaseline) |>
filter(Task == "2")
SSFHRT1 <- SocialStressorFinalHRT1 |>
na.omit()
SocialStressorFinalSCT1 <- data |>
dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb,SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi, SCbaseline) |>
filter(Task == "2")
SSFSCT1 <- SocialStressorFinalSCT1 |>
na.omit()
# Countdown
CountdownFinalHRT1 <- data |>
dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean, HRbaseline) |>
filter(Task == "1")
CDFHRT1 <- CountdownFinalHRT1 |>
na.omit()
CountdownFinalSCT1 <- data |>
dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal,
CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean, SCbaseline) |>
filter(Task == "1")
CDFSCT1 <- CountdownFinalSCT1 |>
na.omit()Male Only
Table 1
These data frames were required to compensate for the missing variables. If I just selected the one column I needed (e.g.,“HRbaseline”) the missing would not match the true sample number because missing values are contained within the survey. This is most evident in the female sample (Female Only Table 1 code chunk).
# Baseline
MaleHRbaseT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline) |>
filter(GenderNumb == "2")
MHRbT1 <- MaleHRbaseT1 |>
na.omit()
MaleSCbaseT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline) |>
filter(GenderNumb == "2")
MSCbT1 <- MaleSCbaseT1 |>
na.omit()
# Social Stressor
MaleSSHRT1 <- SocialStressorFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>
filter(GenderNumb == "2")
MSSHRT1 <- MaleSSHRT1 |>
na.omit()
MaleSSSCT1 <- SocialStressorFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |>
filter(GenderNumb == "2")
MSSSCT1 <- MaleSSSCT1 |>
na.omit()
# Countdown
MaleCDHRT1 <- CountdownFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean) |>
filter(GenderNumb == "2")
MCDHRT1 <- MaleCDHRT1 |>
na.omit()
MaleCDSCT1 <- CountdownFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean) |>
filter(GenderNumb == "2")
MCDSCT1 <- MaleCDSCT1 |>
na.omit()Female Only
# Survey only for distribution checks
FemaleDistribCheck <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal) |>
filter(GenderNumb == "1")
FemaleDisCheck <- FemaleDistribCheck |>
na.omit()Table 1
# baseline
FemaleHRbaselineT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline) |>
filter(GenderNumb == "1")
FemaleHRbaseT1 <- FemaleHRbaselineT1 |>
na.omit()
FemaleSCbaselineT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline) |>
filter(GenderNumb == "1")
FemaleSCbaseT1 <- FemaleSCbaselineT1 |>
na.omit()
# Social Stressor
FemaleSocialSHRT1 <- SocialStressorFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>
filter(GenderNumb == "1")
FemaleSSHRT1 <- FemaleSocialSHRT1 |>
na.omit()
FemaleSocialSSCT1 <- SocialStressorFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |>
filter(GenderNumb == "1")
FemaleSCSST1 <- FemaleSocialSSCT1 |>
na.omit()
# Countdown
FemaleHRCountDT1 <- CountdownFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHeartSignal12, CDHeartUnsig12, HrSigRecovMean, HrUnSigRecovMean) |>
filter(GenderNumb == "1")
FemaleHRCDT1 <- FemaleHRCountDT1 |>
na.omit()
FemaleSCCountDT1 <- CountdownFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDSkinSignal12, CDSkinUnsig12, ScSigRecovMean, ScUnSigRecovMean) |>
filter(GenderNumb == "1")
FemaleSCCDT1 <- FemaleSCCountDT1 |>
na.omit()Scale Reliability Data Frame
Created a new data frame that takes into account the sample and the missing data to calculate the alphas. Can’t use original data frome due to the nature of the autonomic data (i.e., contains a varying amount NAs for all participants, therefore can’t na.omit). To save time and reduce error, I used gsub(). Process below to replicate.
Code <- “Copy and paste the code from the total scores”
new_code <- gsub(“data\$”, ““, code) ^ use an escape character (i.e., \) to treat the $ as normal character and not a special expression. Throws in a”” spot.
new_code2 <- gsub(“\+”, “,”, new_code) ^ same story here.
cat(new_code2) ^ Concatenate the variables into a neat string and copy from console to code chunk.
Scaledf <- data |>
dplyr::select (SRP_01n , SRP_02n , SRP_03n , SRP_04n , SRP5nRev , SRP6nRev , SRP_07n , SRP_08n ,
SRP_09n , SRP_10n , SRP11nRev , SRP_12n , SRP_13n , SRP14nRev ,
SRP_15n , SRP16nRev , SRP_17n , SRP18nRev , SRP19nRev , SRP_20n , SRP21nRev ,
SRP22nRev , SRP23nRev , SRP24nRev , SRP25nRev , SRP26nRev , SRP_27n , SRP_28n ,
SRP_29n , SRP_30n , SRP31nRev , SRP_32n , SRP_33n , SRP34nRev , SRP_35n ,
SRP36nRev , SRP_37n , SRP38nRev , SRP_39n , SRP_40n , SRP_41n , SRP_42n ,
SRP_43n , SRP44nRev , SRP_45n , SRP46nRev , SRP47nRev , SRP_48n , SRP_49n ,
SRP_50n , SRP_51n , SRP_52n , SRP_53n , SRP_54n , SRP_55n , SRP_56n ,
SRP_57n , SRP_58n , SRP_59n , SRP_60n , SRP61nRev , SRP_62n , SRP_63n , SRP_64n ,
ICU_1nRev , ICU_2n , ICU_3nRev , ICU_4n , ICU_5nRev , ICU_6n ,
ICU_7n , ICU_8nRev , ICU_9n , ICU_10n , ICU_11n , ICU_12n , ICU_13nRev ,
ICU_14nRev , ICU_15nRev , ICU_16nRev , ICU_17nRev , ICU_18n , ICU_19nRev ,
ICU_20n , ICU_21n , ICU_22n , ICU_23nRev , ICU_24nRev ,
Lev_01n , Lev_02n , Lev_03nRev , Lev_04n , Lev_05n , Lev_06n , Lev_07nRev , Lev_08n ,
Lev_09n , Lev_10nRev , Lev_11n , Lev_12n , Lev_13nRev , Lev_16n , Lev_17n , Lev_18n ,
Lev_19n , Lev_20n , Lev_21nRev , Lev_22n , Lev_23n , Lev_24n , Lev_25n , Lev_26nRev ,
ZSSS_1nRev , ZSSS_2n , ZSSS_3nRev , ZSSS_4n , ZSSS_5nRev , ZSSS_6nRev , ZSSS_7n , ZSSS_8nRev ,
ZSSS_9nRev , ZSSS_10n , ZSSS_11n , ZSSS_12n , ZSSS_13n , ZSSS_14nRev , ZSSS_15n , ZSSS_16nRev ,
ZSSS_17nRev , ZSSS_18nRev , ZSSS_19n , ZSSS_20n , ZSSS_21n , ZSSS_22nRev , ZSSS_23nRev , ZSSS_24nRev ,
ZSSS_25n , ZSSS_26n , ZSSS_27n , ZSSS_28nRev , ZSSS_29nRev , ZSSS_30n , ZSSS_31n ,
ZSSS_32nRev , ZSSS_33n , ZSSS_34nRev , ZSSS_35n , ZSSS_36nRev , ZSSS_37n , ZSSS_38n , ZSSS_39nRev , ZSSS_40n) |>
na.omit()
# SRP
SRPTotA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
"SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev",
"SRP_02n","SRP_07n", "SRP11nRev", "SRP_15n", "SRP19nRev", "SRP23nRev", "SRP26nRev", "SRP_30n", "SRP_33n",
"SRP_37n", "SRP_40n", "SRP44nRev", "SRP_48n", "SRP_01n", "SRP_53n", "SRP_56n", "SRP_60n", "SRP_04n",
"SRP_09n", "SRP14nRev", "SRP_17n", "SRP22nRev", "SRP25nRev", "SRP_28n", "SRP_32n", "SRP36nRev", "SRP_39n",
"SRP_42n", "SRP47nRev", "SRP_51n", "SRP_55n", "SRP_59n", "SRP5nRev", "SRP6nRev", "SRP_10n", "SRP_12n",
"SRP18nRev", "SRP21nRev", "SRP_29n", "SRP34nRev", "SRP_43n", "SRP46nRev", "SRP_49n", "SRP_52n",
"SRP_57n", "SRP_62n", "SRP_63n", "SRP_64n")]
SRPIPMA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
"SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev")]
SRPICAA <-Scaledf[ , c("SRP_02n","SRP_07n","SRP11nRev","SRP_15n","SRP19nRev","SRP23nRev","SRP26nRev",
"SRP_30n","SRP_33n", "SRP_37n","SRP_40n", "SRP44nRev","SRP_48n", "SRP_53n","SRP_56n", "SRP_60n")]
SRPELSA <-Scaledf[ , c("SRP_01n","SRP_04n","SRP_09n","SRP14nRev","SRP_17n","SRP22nRev","SRP25nRev",
"SRP_28n","SRP_32n", "SRP36nRev","SRP_39n", "SRP_42n","SRP47nRev", "SRP_51n","SRP_55n", "SRP_59n")]
SRPASBA <-Scaledf[ , c("SRP5nRev","SRP6nRev","SRP_10n","SRP_12n","SRP18nRev","SRP21nRev","SRP_29n",
"SRP34nRev","SRP_43n", "SRP46nRev","SRP_49n", "SRP_52n","SRP_57n", "SRP_62n","SRP_63n", "SRP_64n")]
# ICU
ICUTotA <-Scaledf[ , c("ICU_1nRev","ICU_2n","ICU_3nRev","ICU_4n","ICU_5nRev","ICU_6n","ICU_7n",
"ICU_8nRev","ICU_9n", "ICU_10n", "ICU_11n","ICU_12n", "ICU_13nRev","ICU_14nRev", "ICU_15nRev",
"ICU_16nRev","ICU_17nRev", "ICU_18n","ICU_19nRev", "ICU_20n","ICU_21n", "ICU_22n","ICU_23nRev", "ICU_24nRev")]
ICUCalA <-Scaledf[ , c("ICU_4n","ICU_8nRev","ICU_9n","ICU_18n","ICU_11n","ICU_21n","ICU_7n",
"ICU_20n","ICU_2n", "ICU_12n","ICU_10n")]
ICUUncareA <-Scaledf[ , c("ICU_15nRev","ICU_23nRev","ICU_16nRev","ICU_3nRev","ICU_17nRev","ICU_24nRev","ICU_13nRev",
"ICU_5nRev")]
ICUUnemoA <-Scaledf[ , c("ICU_1nRev","ICU_19nRev","ICU_6n","ICU_22n","ICU_14nRev")]
# LSRP
LevTotA <-Scaledf[ , c("Lev_01n","Lev_02n","Lev_03nRev","Lev_04n","Lev_05n","Lev_06n","Lev_07nRev",
"Lev_08n","Lev_09n", "Lev_10nRev","Lev_11n","Lev_12n", "Lev_13nRev","Lev_16n","Lev_17n", "Lev_18n",
"Lev_19n", "Lev_20n","Lev_21nRev", "Lev_22n","Lev_23n", "Lev_24n", "Lev_25n","Lev_26nRev" )]
LevPrimA <-Scaledf[ , c("Lev_02n","Lev_04n","Lev_07nRev","Lev_09n","Lev_11n","Lev_12n",
"Lev_13nRev","Lev_17n", "Lev_19n","Lev_21nRev","Lev_22n", "Lev_23n","Lev_24n","Lev_25n", "Lev_26nRev")]
LevSecA <-Scaledf[ , c("Lev_01n","Lev_03nRev","Lev_05n","Lev_06n","Lev_08n","Lev_10nRev",
"Lev_16n","Lev_18n", "Lev_20n")]
# SSS
SSSTotA <-Scaledf[ , c("ZSSS_1nRev","ZSSS_2n","ZSSS_3nRev","ZSSS_4n","ZSSS_5nRev","ZSSS_6nRev","ZSSS_7n", "ZSSS_8nRev","ZSSS_9nRev", "ZSSS_10n",
"ZSSS_11n", "ZSSS_12n","ZSSS_13n", "ZSSS_14nRev","ZSSS_15n", "ZSSS_16nRev", "ZSSS_17nRev","ZSSS_18nRev", "ZSSS_19n", "ZSSS_20n",
"ZSSS_21n", "ZSSS_22nRev", "ZSSS_23nRev", "ZSSS_24nRev", "ZSSS_25n", "ZSSS_26n", "ZSSS_27n","ZSSS_28nRev","ZSSS_29nRev","ZSSS_30n","ZSSS_31n",
"ZSSS_32nRev","ZSSS_33n","ZSSS_34nRev","ZSSS_35n","ZSSS_36nRev","ZSSS_37n","ZSSS_38n", "ZSSS_39nRev", "ZSSS_40n")]
SSSDISA <-Scaledf[ , c("ZSSS_12n","ZSSS_13n","ZSSS_25n","ZSSS_30n","ZSSS_33n","ZSSS_35n",
"ZSSS_1nRev","ZSSS_29nRev", "ZSSS_32nRev", "ZSSS_36nRev")]
SSSBorA <-Scaledf[ , c("ZSSS_2n","ZSSS_7n","ZSSS_15n","ZSSS_27n","ZSSS_31n","ZSSS_5nRev",
"ZSSS_8nRev","ZSSS_24nRev", "ZSSS_34nRev", "ZSSS_39nRev")]
SSSThrilA <-Scaledf[ , c("ZSSS_11n","ZSSS_20n","ZSSS_21n","ZSSS_38n","ZSSS_40n","ZSSS_3nRev",
"ZSSS_16nRev","ZSSS_17nRev", "ZSSS_23nRev", "ZSSS_28nRev")]
SSSExpA <-Scaledf[ , c("ZSSS_4n","ZSSS_10n","ZSSS_19n","ZSSS_26n","ZSSS_37n","ZSSS_6nRev",
"ZSSS_9nRev","ZSSS_14nRev", "ZSSS_18nRev", "ZSSS_22nRev")]Graph Data Wrangling
Countdown
CDGraphHR <- data |>
dplyr::select(Task, HrStr_00_07:HrStr_00_37, HrStr_01_37: HrStr_02_07,
HrStr_00_52:HrStr_01_22, HrStr_02_22:HrStr_02_52) |>
dplyr::filter(Task == "1")
CDGraphSC <- data |>
dplyr::select(Task, ScStr_00_07:ScStr_00_37, ScStr_01_37: ScStr_02_07,
ScStr_00_52:ScStr_01_22, ScStr_02_22:ScStr_02_52) |>
dplyr::filter(Task == "1")
# HR
CDGraphHRSig <- CDGraphHR |>
dplyr::select(HrStr_00_07:HrStr_00_37, HrStr_01_37:HrStr_02_07)
CDGraphHRUnsig <- CDGraphHR |>
dplyr::select(HrStr_00_52:HrStr_01_22, HrStr_02_22:HrStr_02_52)
# SC
CDGraphSCSig <- CDGraphSC |>
dplyr::select(ScStr_00_07:ScStr_00_37, ScStr_01_37:ScStr_02_07)
CDGraphSCUnsig <- CDGraphSC |>
dplyr::select(ScStr_00_52:ScStr_01_22, ScStr_02_22:ScStr_02_52)Function 2
Function that divides the data frame in half then creates a new column with the mean of the two columns (i.e., column 1 and column 32, column 2 and column 33, etc.).
Mean_Phase <- function(df) {
# Run a check
num_cols <- ncol(df)
if (num_cols %% 2 != 0) {
stop("Something is off.")
}
# Loop through half of the columns to create the mean columns
for (i in 1:(num_cols / 2)) {
col1 <- df[, i]
col2 <- df[, i + (num_cols / 2)]
new_col_name <- paste0("Mean_", names(df)[i], "_", names(df)[i + (num_cols / 2)])
df[[new_col_name]] <- rowMeans(cbind(col1, col2))
}
return(df)
}# HR Signaled
CDGraphHRSig1 <- Mean_Phase(CDGraphHRSig)
CDGraphHRSignaled <- CDGraphHRSig1 |>
dplyr::select(cols = 63:93)
# HR Unsignaled
CDGraphHRUnsig1 <- Mean_Phase(CDGraphHRUnsig)
CDGraphHRUnsignaled <- CDGraphHRUnsig1 |>
dplyr::select(cols = 63:93)
# SC Signaled
CDGraphSCSig1 <- Mean_Phase(CDGraphSCSig)
CDGraphSCSignaled <- CDGraphSCSig1 |>
dplyr::select(cols = 63:93)
# SC Unsignaled
CDGraphSCUnsig1 <- Mean_Phase(CDGraphSCUnsig)
CDGraphSCUnsignaled <- CDGraphSCUnsig1 |>
dplyr::select(cols = 63:93)HR
# HRSig
mean_values <- colMeans(CDGraphHRSignaled, na.rm = TRUE)
# Convert mean_values to a data frame for plotting
CDGraphHRSignaled1 <- data.frame(
time = seq_len(length(mean_values)),
value = mean_values)
# HRUnsig
mean_values <- colMeans(CDGraphHRUnsignaled, na.rm = TRUE)
CDGraphHRUnsignaled1 <- data.frame(
time = seq_len(length(mean_values)),
value = mean_values)
# Merge the two
CDGraphHR <- dplyr::bind_rows(
CDGraphHRSignaled1 |>
dplyr::mutate(group = "Signaled"),
CDGraphHRUnsignaled1 |>
dplyr::mutate(group = "Unsignaled")
)
# Reset rownames
rownames(CDGraphHR) <- 1:nrow(CDGraphHR)SC
# SCSig
mean_values <- colMeans(CDGraphSCSignaled, na.rm = TRUE)
CDGraphSCSignaled1 <- data.frame(
time = seq_len(length(mean_values)),
value = mean_values)
# SCUnsig
mean_values <- colMeans(CDGraphSCUnsignaled, na.rm = TRUE)
CDGraphSCUnsignaled1 <- data.frame(
time = seq_len(length(mean_values)),
value = mean_values)
# Merge the two
CDGraphSC <- dplyr::bind_rows(
CDGraphSCSignaled1 |>
dplyr::mutate(group = "Signaled"),
CDGraphSCUnsignaled1 |>
dplyr::mutate(group = "Unsignaled")
)
# Reset rownames
rownames(CDGraphSC) <- 1:nrow(CDGraphSC)Analysis
Reliabity Scores
Note: ICU callousness, SSS boredom and experience seeking were dropped because of their low alpha (02-09-24).
# SRP
cronbach.alpha(SRPTotA)
Cronbach's alpha for the 'SRPTotA' data-set
Items: 64
Sample units: 92
alpha: 0.884cronbach.alpha(SRPIPMA)
Cronbach's alpha for the 'SRPIPMA' data-set
Items: 16
Sample units: 92
alpha: 0.797cronbach.alpha(SRPICAA)
Cronbach's alpha for the 'SRPICAA' data-set
Items: 16
Sample units: 92
alpha: 0.752cronbach.alpha(SRPELSA)
Cronbach's alpha for the 'SRPELSA' data-set
Items: 16
Sample units: 92
alpha: 0.788cronbach.alpha(SRPASBA)
Cronbach's alpha for the 'SRPASBA' data-set
Items: 16
Sample units: 92
alpha: 0.713# ICU
cronbach.alpha(ICUTotA)
Cronbach's alpha for the 'ICUTotA' data-set
Items: 24
Sample units: 92
alpha: 0.802cronbach.alpha(ICUCalA)
Cronbach's alpha for the 'ICUCalA' data-set
Items: 11
Sample units: 92
alpha: 0.395cronbach.alpha(ICUUncareA)
Cronbach's alpha for the 'ICUUncareA' data-set
Items: 8
Sample units: 92
alpha: 0.778cronbach.alpha(ICUUnemoA)
Cronbach's alpha for the 'ICUUnemoA' data-set
Items: 5
Sample units: 92
alpha: 0.888# LSRP
cronbach.alpha(LevTotA)
Cronbach's alpha for the 'LevTotA' data-set
Items: 24
Sample units: 92
alpha: 0.827cronbach.alpha(LevPrimA)
Cronbach's alpha for the 'LevPrimA' data-set
Items: 15
Sample units: 92
alpha: 0.804cronbach.alpha(LevSecA)
Cronbach's alpha for the 'LevSecA' data-set
Items: 9
Sample units: 92
alpha: 0.664# ZSSS
cronbach.alpha(SSSTotA)
Cronbach's alpha for the 'SSSTotA' data-set
Items: 40
Sample units: 92
alpha: 0.751cronbach.alpha(SSSDISA)
Cronbach's alpha for the 'SSSDISA' data-set
Items: 10
Sample units: 92
alpha: 0.674cronbach.alpha(SSSBorA)
Cronbach's alpha for the 'SSSBorA' data-set
Items: 10
Sample units: 92
alpha: 0.483cronbach.alpha(SSSThrilA)
Cronbach's alpha for the 'SSSThrilA' data-set
Items: 10
Sample units: 92
alpha: 0.8cronbach.alpha(SSSExpA)
Cronbach's alpha for the 'SSSExpA' data-set
Items: 10
Sample units: 92
alpha: 0.425Table 1 (Descriptives)
Survey Means
# full
FSDescriptives <- FSFSurveyT1 |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore, LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,SSSThrilTotal
),
.fns = c( # this is used to describe the function within a list of the output (i.e., mean and sd), and the "\" is just shorthand for "function"
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T),
min = \(x) min(x, na.rm = T),
max = \(x) max(x, na.rm = T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptives)| name | value |
|---|---|
| SRPTotalScore —- mean | 141.554348 |
| SRPTotalScore —- sd | 22.906098 |
| SRPTotalScore —- min | 74.000000 |
| SRPTotalScore —- max | 195.000000 |
| SRPIPMTotal —- mean | 37.684783 |
| SRPIPMTotal —- sd | 7.609897 |
| SRPIPMTotal —- min | 18.000000 |
| SRPIPMTotal —- max | 57.000000 |
| SRPCATotal —- mean | 36.945652 |
| SRPCATotal —- sd | 7.564178 |
| SRPCATotal —- min | 20.000000 |
| SRPCATotal —- max | 57.000000 |
| SRPELSTotal —- mean | 42.163044 |
| SRPELSTotal —- sd | 8.894099 |
| SRPELSTotal —- min | 16.000000 |
| SRPELSTotal —- max | 61.000000 |
| SRPASBTotal —- mean | 24.760870 |
| SRPASBTotal —- sd | 6.947795 |
| SRPASBTotal —- min | 16.000000 |
| SRPASBTotal —- max | 42.000000 |
| ICUTotScore —- mean | 42.717391 |
| ICUTotScore —- sd | 7.124214 |
| ICUTotScore —- min | 30.000000 |
| ICUTotScore —- max | 57.000000 |
| ICUUncareTotalScore —- mean | 14.315217 |
| ICUUncareTotalScore —- sd | 3.673275 |
| ICUUncareTotalScore —- min | 8.000000 |
| ICUUncareTotalScore —- max | 24.000000 |
| ICUUnemoTotal —- mean | 12.869565 |
| ICUUnemoTotal —- sd | 3.911711 |
| ICUUnemoTotal —- min | 5.000000 |
| ICUUnemoTotal —- max | 20.000000 |
| LevTotalScore —- mean | 46.532609 |
| LevTotalScore —- sd | 7.609206 |
| LevTotalScore —- min | 27.000000 |
| LevTotalScore —- max | 61.000000 |
| LevPrimTotalScore —- mean | 28.217391 |
| LevPrimTotalScore —- sd | 5.232666 |
| LevPrimTotalScore —- min | 18.000000 |
| LevPrimTotalScore —- max | 38.000000 |
| LevSecTotalScore —- mean | 18.315217 |
| LevSecTotalScore —- sd | 3.563951 |
| LevSecTotalScore —- min | 9.000000 |
| LevSecTotalScore —- max | 27.000000 |
| SSSTotalScore —- mean | 17.119565 |
| SSSTotalScore —- sd | 5.516892 |
| SSSTotalScore —- min | 2.000000 |
| SSSTotalScore —- max | 29.000000 |
| SSSDISTotal —- mean | 3.978261 |
| SSSDISTotal —- sd | 2.362613 |
| SSSDISTotal —- min | 0.000000 |
| SSSDISTotal —- max | 9.000000 |
| SSSThrilTotal —- mean | 6.086957 |
| SSSThrilTotal —- sd | 2.884792 |
| SSSThrilTotal —- min | 0.000000 |
| SSSThrilTotal —- max | 10.000000 |
# female
FSDescriptivesFemale <- FSFSurveyT1 |>
filter(GenderNumb == "1") |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,
SSSThrilTotal
),
.fns = c(
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T),
min = \(x) min(x, na.rm = T),
max = \(x) max(x, na.rm = T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptivesFemale)| name | value |
|---|---|
| SRPTotalScore —- mean | 138.985916 |
| SRPTotalScore —- sd | 23.912634 |
| SRPTotalScore —- min | 74.000000 |
| SRPTotalScore —- max | 194.000000 |
| SRPIPMTotal —- mean | 37.126761 |
| SRPIPMTotal —- sd | 8.095730 |
| SRPIPMTotal —- min | 18.000000 |
| SRPIPMTotal —- max | 57.000000 |
| SRPCATotal —- mean | 35.295775 |
| SRPCATotal —- sd | 6.776840 |
| SRPCATotal —- min | 20.000000 |
| SRPCATotal —- max | 54.000000 |
| SRPELSTotal —- mean | 41.915493 |
| SRPELSTotal —- sd | 9.401742 |
| SRPELSTotal —- min | 16.000000 |
| SRPELSTotal —- max | 61.000000 |
| SRPASBTotal —- mean | 24.647887 |
| SRPASBTotal —- sd | 6.911892 |
| SRPASBTotal —- min | 16.000000 |
| SRPASBTotal —- max | 42.000000 |
| ICUTotScore —- mean | 41.661972 |
| ICUTotScore —- sd | 7.197110 |
| ICUTotScore —- min | 30.000000 |
| ICUTotScore —- max | 57.000000 |
| ICUUncareTotalScore —- mean | 14.070422 |
| ICUUncareTotalScore —- sd | 3.896240 |
| ICUUncareTotalScore —- min | 8.000000 |
| ICUUncareTotalScore —- max | 24.000000 |
| ICUUnemoTotal —- mean | 12.380282 |
| ICUUnemoTotal —- sd | 3.822363 |
| ICUUnemoTotal —- min | 5.000000 |
| ICUUnemoTotal —- max | 20.000000 |
| LevTotalScore —- mean | 45.788732 |
| LevTotalScore —- sd | 7.882921 |
| LevTotalScore —- min | 27.000000 |
| LevTotalScore —- max | 61.000000 |
| LevPrimTotalScore —- mean | 27.591549 |
| LevPrimTotalScore —- sd | 5.172945 |
| LevPrimTotalScore —- min | 18.000000 |
| LevPrimTotalScore —- max | 37.000000 |
| LevSecTotalScore —- mean | 18.197183 |
| LevSecTotalScore —- sd | 3.804583 |
| LevSecTotalScore —- min | 9.000000 |
| LevSecTotalScore —- max | 27.000000 |
| SSSTotalScore —- mean | 16.774648 |
| SSSTotalScore —- sd | 5.695104 |
| SSSTotalScore —- min | 2.000000 |
| SSSTotalScore —- max | 29.000000 |
| SSSDISTotal —- mean | 4.000000 |
| SSSDISTotal —- sd | 2.420154 |
| SSSDISTotal —- min | 0.000000 |
| SSSDISTotal —- max | 9.000000 |
| SSSThrilTotal —- mean | 5.718310 |
| SSSThrilTotal —- sd | 2.889306 |
| SSSThrilTotal —- min | 0.000000 |
| SSSThrilTotal —- max | 10.000000 |
# male
FSDescriptivesMale <- FSFSurveyT1 |>
filter(GenderNumb == "2") |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal,
SSSThrilTotal
),
.fns = c(
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptivesMale)| name | value |
|---|---|
| SRPTotalScore —- mean | 150.238095 |
| SRPTotalScore —- sd | 16.834205 |
| SRPIPMTotal —- mean | 39.571429 |
| SRPIPMTotal —- sd | 5.408987 |
| SRPCATotal —- mean | 42.523809 |
| SRPCATotal —- sd | 7.567160 |
| SRPELSTotal —- mean | 43.000000 |
| SRPELSTotal —- sd | 7.042727 |
| SRPASBTotal —- mean | 25.142857 |
| SRPASBTotal —- sd | 7.226934 |
| ICUTotScore —- mean | 46.285714 |
| ICUTotScore —- sd | 5.684566 |
| ICUUncareTotalScore —- mean | 15.142857 |
| ICUUncareTotalScore —- sd | 2.707133 |
| ICUUnemoTotal —- mean | 14.523810 |
| ICUUnemoTotal —- sd | 3.842122 |
| LevTotalScore —- mean | 49.047619 |
| LevTotalScore —- sd | 6.111270 |
| LevPrimTotalScore —- mean | 30.333333 |
| LevPrimTotalScore —- sd | 4.983306 |
| LevSecTotalScore —- mean | 18.714286 |
| LevSecTotalScore —- sd | 2.629503 |
| SSSTotalScore —- mean | 18.285714 |
| SSSTotalScore —- sd | 4.807732 |
| SSSDISTotal —- mean | 3.904762 |
| SSSDISTotal —- sd | 2.211442 |
| SSSThrilTotal —- mean | 7.333333 |
| SSSThrilTotal —- sd | 2.556039 |
ANS Means
As mentioned in the manuscript, some individuals SC that exceeded the maximum threshold of 9.99 of the NeuLog instrument. Therefore, there is sample number variation between HR, SC. Additionally, there are two tasks present which subdivided the sample further.
# Full
## Baseline
stat.desc(FSFHRT1$HRbaseline) nbr.val nbr.null nbr.na min max range
92.0000000 0.0000000 0.0000000 43.5285714 118.4214286 74.8928571
sum median mean SE.mean CI.mean.0.95 var
6637.2500000 68.6571429 72.1440217 1.4811676 2.9421576 201.8348960
std.dev coef.var
14.2068609 0.1969236 stat.desc(FSFSCT1$SCbaseline) nbr.val nbr.null nbr.na min max range
89.0000000 0.0000000 0.0000000 0.1522171 4.8776707 4.7254536
sum median mean SE.mean CI.mean.0.95 var
146.1194671 1.3072307 1.6417918 0.1228692 0.2441768 1.3436196
std.dev coef.var
1.1591461 0.7060250 ## Social Stressor
stat.desc(SSFHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
43.000000 0.000000 0.000000 -4320.742857 4673.521429
range sum median mean SE.mean
8994.264286 22290.600000 500.842857 518.386047 317.340451
CI.mean.0.95 var std.dev coef.var
640.418957 4330313.347240 2080.940496 4.014268 stat.desc(SSFSCT1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
41.0000000 0.0000000 0.0000000 -0.6968471 1058.9069914
range sum median mean SE.mean
1059.6038386 16824.2078307 373.5585014 410.3465325 40.1657903
CI.mean.0.95 var std.dev coef.var
81.1780903 66144.9190668 257.1865453 0.6267545 ## Countdown
stat.desc(CDFHRT1$CDHeartSignal12) nbr.val nbr.null nbr.na min max range
49.0000000 1.0000000 0.0000000 -19.8000000 14.8083333 34.6083333
sum median mean SE.mean CI.mean.0.95 var
-5.0166667 -0.2166667 -0.1023810 0.8030556 1.6146515 31.6000174
std.dev coef.var
5.6213893 -54.9065929 stat.desc(CDFHRT1$CDHeartUnsig12) nbr.val nbr.null nbr.na min max range
49.0000000 1.0000000 0.0000000 -11.7333333 11.5500000 23.2833333
sum median mean SE.mean CI.mean.0.95 var
-17.7500000 -0.6000000 -0.3622449 0.5812107 1.1686024 16.5524858
std.dev coef.var
4.0684746 -11.2312821 stat.desc(CDFHRT1$HrSigRecovMean) nbr.val nbr.null nbr.na min max range
49.0000000 1.0000000 0.0000000 -23.0833333 9.5416667 32.6250000
sum median mean SE.mean CI.mean.0.95 var
-83.7750000 -0.1250000 -1.7096939 0.8933322 1.7961648 39.1040809
std.dev coef.var
6.2533256 -3.6575703 stat.desc(CDFHRT1$HrUnSigRecovMean) nbr.val nbr.null nbr.na min max range
49.0000000 1.0000000 0.0000000 -11.5750000 9.4750000 21.0500000
sum median mean SE.mean CI.mean.0.95 var
-49.2916667 -0.8666667 -1.0059524 0.7027512 1.4129759 24.1991001
std.dev coef.var
4.9192581 -4.8901500 stat.desc(CDFSCT1$CDSkinSignal12) nbr.val nbr.null nbr.na min max range
48.00000000 0.00000000 0.00000000 -0.04290417 0.49067917 0.53358333
sum median mean SE.mean CI.mean.0.95 var
3.95463667 0.04147292 0.08238826 0.01447807 0.02912613 0.01006150
std.dev coef.var
0.10030704 1.21749186 stat.desc(CDFSCT1$CDSkinUnsig12) nbr.val nbr.null nbr.na min max
48.000000000 0.000000000 0.000000000 -0.218163333 0.305742500
range sum median mean SE.mean
0.523905833 -0.332931667 -0.004957083 -0.006936076 0.011432141
CI.mean.0.95 var std.dev coef.var
0.022998500 0.006273304 0.079204193 -11.419163920 stat.desc(CDFSCT1$ScSigRecovMean) nbr.val nbr.null nbr.na min max range
48.00000000 0.00000000 0.00000000 -0.05321750 1.46255250 1.51577000
sum median mean SE.mean CI.mean.0.95 var
11.07441500 0.12455458 0.23071698 0.04656247 0.09367161 0.10406705
std.dev coef.var
0.32259426 1.39822503 stat.desc(CDFSCT1$ScUnSigRecovMean) nbr.val nbr.null nbr.na min max range
48.00000000 0.00000000 0.00000000 -0.10906250 0.94537500 1.05443750
sum median mean SE.mean CI.mean.0.95 var
9.32657500 0.14871833 0.19430365 0.03158500 0.06354083 0.04788540
std.dev coef.var
0.21882732 1.12621314 # Male
## Baseline
stat.desc(MHRbT1$HRbaseline) nbr.val nbr.null nbr.na min max range
21.0000000 0.0000000 0.0000000 43.5285714 96.2500000 52.7214286
sum median mean SE.mean CI.mean.0.95 var
1382.1714286 65.5000000 65.8176871 2.7980315 5.8365915 164.4085899
std.dev coef.var
12.8221913 0.1948138 stat.desc(MSCbT1$SCbaseline) nbr.val nbr.null nbr.na min max range
20.0000000 0.0000000 0.0000000 0.3198686 4.8776707 4.5578021
sum median mean SE.mean CI.mean.0.95 var
38.9497236 1.5946993 1.9474862 0.2736521 0.5727604 1.4977091
std.dev coef.var
1.2238092 0.6284046 ## Social Stressor
stat.desc(MSSHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
10.00000 0.00000 0.00000 -2724.00000 4673.52143
range sum median mean SE.mean
7397.52143 4609.68571 294.56071 460.96857 748.57964
CI.mean.0.95 var std.dev coef.var
1693.40480 5603714.82271 2367.21668 5.13531 stat.desc(MSSSCT1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
9.0000000 0.0000000 0.0000000 123.5120343 1058.9069914
range sum median mean SE.mean
935.3949571 4066.5755379 418.2931671 451.8417264 99.4922953
CI.mean.0.95 var std.dev coef.var
229.4296444 89088.4514317 298.4768859 0.6605784 ## Countdown
stat.desc(MCDHRT1$CDHeartSignal12) nbr.val nbr.null nbr.na min max range
11.000000 0.000000 0.000000 -19.800000 6.075000 25.875000
sum median mean SE.mean CI.mean.0.95 var
-55.783333 -4.666667 -5.071212 2.024427 4.510704 45.081352
std.dev coef.var
6.714265 -1.323996 stat.desc(MCDHRT1$CDHeartUnsig12) nbr.val nbr.null nbr.na min max range
11.000000 0.000000 0.000000 -11.733333 3.208333 14.941667
sum median mean SE.mean CI.mean.0.95 var
-20.416667 -1.416667 -1.856061 1.213596 2.704061 16.200973
std.dev coef.var
4.025043 -2.168595 stat.desc(MCDHRT1$HrSigRecovMean) nbr.val nbr.null nbr.na min max range
11.00000000 0.00000000 0.00000000 -5.29166667 6.07500000 11.36666667
sum median mean SE.mean CI.mean.0.95 var
0.66666667 0.66666667 0.06060606 1.27778370 2.84707950 17.96004293
std.dev coef.var
4.23792908 69.92582990 stat.desc(MCDHRT1$HrUnSigRecovMean) nbr.val nbr.null nbr.na min max range
11.0000000 0.0000000 0.0000000 -9.0250000 9.4000000 18.4250000
sum median mean SE.mean CI.mean.0.95 var
4.4166667 0.5333333 0.4015152 1.6214210 3.6127511 28.9190669
std.dev coef.var
5.3776451 13.3933803 stat.desc(MCDSCT1$CDSkinSignal12) nbr.val nbr.null nbr.na min max range
11.00000000 0.00000000 0.00000000 -0.04290417 0.19242833 0.23533250
sum median mean SE.mean CI.mean.0.95 var
0.56964583 0.03981917 0.05178598 0.01889151 0.04209290 0.00392578
std.dev coef.var
0.06265605 1.20990354 stat.desc(MCDSCT1$CDSkinUnsig12) nbr.val nbr.null nbr.na min max
11.0000000000 0.0000000000 0.0000000000 -0.1759658333 0.1112358333
range sum median mean SE.mean
0.2872016667 0.0255025000 0.0007483333 0.0023184091 0.0265923046
CI.mean.0.95 var std.dev coef.var
0.0592513470 0.0077786573 0.0881966967 38.0419042627 stat.desc(MCDSCT1$ScSigRecovMean) nbr.val nbr.null nbr.na min max range
11.00000000 0.00000000 0.00000000 -0.02178917 1.04046583 1.06225500
sum median mean SE.mean CI.mean.0.95 var
2.16133917 0.09930583 0.19648538 0.08808900 0.19627452 0.08535639
std.dev coef.var
0.29215815 1.48692057 stat.desc(MCDSCT1$ScUnSigRecovMean) nbr.val nbr.null nbr.na min max range
11.00000000 0.00000000 0.00000000 0.00062750 0.75934333 0.75871583
sum median mean SE.mean CI.mean.0.95 var
1.94321417 0.15610000 0.17665583 0.06429570 0.14325974 0.04547331
std.dev coef.var
0.21324471 1.20711954 # Female
## Baseline
stat.desc(FemaleHRbaseT1$HRbaseline) nbr.val nbr.null nbr.na min max range
71.0000000 0.0000000 0.0000000 49.6000000 118.4214286 68.8214286
sum median mean SE.mean CI.mean.0.95 var
5255.0785714 72.6500000 74.0151911 1.6777476 3.3461621 199.8534358
std.dev coef.var
14.1369528 0.1910007 stat.desc(FemaleSCbaseT1$SCbaseline) nbr.val nbr.null nbr.na min max range
69.0000000 0.0000000 0.0000000 0.1522171 4.7345321 4.5823150
sum median mean SE.mean CI.mean.0.95 var
107.1697436 1.1533514 1.5531847 0.1364600 0.2723018 1.2848726
std.dev coef.var
1.1335222 0.7298052 ## Social Stressor
stat.desc(FemaleSSHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
33.000000 0.000000 0.000000 -4320.742857 4346.685714
range sum median mean SE.mean
8667.428571 17680.914286 500.842857 535.785281 352.744608
CI.mean.0.95 var std.dev coef.var
718.517255 4106149.041058 2026.363502 3.782044 stat.desc(FemaleSCSST1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
32.0000000 0.0000000 0.0000000 -0.6968471 914.7494050
range sum median mean SE.mean
915.4462521 12757.6322929 364.8510800 398.6760092 43.9165500
CI.mean.0.95 var std.dev coef.var
89.5683942 61717.2275955 248.4295224 0.6231364 ## Countdown
stat.desc(FemaleHRCDT1$CDHeartSignal12) nbr.val nbr.null nbr.na min max range
38.00000000 1.00000000 0.00000000 -5.57500000 14.80833333 20.38333333
sum median mean SE.mean CI.mean.0.95 var
50.76666667 -0.07083333 1.33596491 0.71350981 1.44570819 19.34565730
std.dev coef.var
4.39836985 3.29227946 stat.desc(FemaleHRCDT1$CDHeartUnsig12) nbr.val nbr.null nbr.na min max range
38.00000000 1.00000000 0.00000000 -9.58333333 11.55000000 21.13333333
sum median mean SE.mean CI.mean.0.95 var
2.66666667 -0.17916667 0.07017544 0.65372210 1.32456678 16.23939802
std.dev coef.var
4.02981365 57.42484445 stat.desc(FemaleHRCDT1$HrSigRecovMean) nbr.val nbr.null nbr.na min max range
38.000000 1.000000 0.000000 -23.083333 9.541667 32.625000
sum median mean SE.mean CI.mean.0.95 var
-84.441667 -0.650000 -2.222149 1.084267 2.196934 44.674126
std.dev coef.var
6.683871 -3.007841 stat.desc(FemaleHRCDT1$HrUnSigRecovMean) nbr.val nbr.null nbr.na min max range
38.0000000 1.0000000 0.0000000 -11.5750000 9.4750000 21.0500000
sum median mean SE.mean CI.mean.0.95 var
-53.7083333 -1.1291667 -1.4133772 0.7749032 1.5701029 22.8180463
std.dev coef.var
4.7768239 -3.3797233 stat.desc(FemaleSCCDT1$CDSkinSignal12) nbr.val nbr.null nbr.na min max range
37.00000000 0.00000000 0.00000000 -0.01934167 0.49067917 0.51002083
sum median mean SE.mean CI.mean.0.95 var
3.38499083 0.08083167 0.09148624 0.01776280 0.03602463 0.01167413
std.dev coef.var
0.10804689 1.18101797 stat.desc(FemaleSCCDT1$CDSkinUnsig12) nbr.val nbr.null nbr.na min max range
37.000000000 0.000000000 0.000000000 -0.218163333 0.305742500 0.523905833
sum median mean SE.mean CI.mean.0.95 var
-0.358434167 -0.007892500 -0.009687410 0.012729472 0.025816565 0.005995459
std.dev coef.var
0.077430352 -7.992884904 stat.desc(FemaleSCCDT1$ScSigRecovMean) nbr.val nbr.null nbr.na min max range
37.00000000 0.00000000 0.00000000 -0.05321750 1.46255250 1.51577000
sum median mean SE.mean CI.mean.0.95 var
8.91307583 0.13283500 0.24089394 0.05494241 0.11142836 0.11169071
std.dev coef.var
0.33420161 1.38733920 stat.desc(FemaleSCCDT1$ScUnSigRecovMean) nbr.val nbr.null nbr.na min max range
37.00000000 0.00000000 0.00000000 -0.10906250 0.94537500 1.05443750
sum median mean SE.mean CI.mean.0.95 var
7.38336083 0.14172083 0.19955029 0.03667318 0.07437665 0.04976211
std.dev coef.var
0.22307424 1.11788479 t-tests
ANS
# baseline
ind.t.test1<- t.test(HRbaseline ~ Gender, data = FSFHRT1)
ind.t.test1
Welch Two Sample t-test
data: HRbaseline by Gender
t = 2.5127, df = 35.65, p-value = 0.01665
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
1.578621 14.816388
sample estimates:
mean in group Female mean in group Male
74.01519 65.81769 ind.t.test1<- t.test(SCbaseline ~ Gender, data = FSFSCT1)
ind.t.test1
Welch Two Sample t-test
data: SCbaseline by Gender
t = -1.2895, df = 29.121, p-value = 0.2074
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.019597 0.230994
sample estimates:
mean in group Female mean in group Male
1.553185 1.947486 #SS
ind.t.test1<- t.test(SSHRCombAUCi ~ Gender, data = SSFHRT1)
ind.t.test1
Welch Two Sample t-test
data: SSHRCombAUCi by Gender
t = 0.09041, df = 13.257, p-value = 0.9293
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1709.432 1859.066
sample estimates:
mean in group Female mean in group Male
535.7853 460.9686 ind.t.test1<- t.test(SSSCCombAUCi ~ Gender, data = SSFSCT1)
ind.t.test1
Welch Two Sample t-test
data: SSSCCombAUCi by Gender
t = -0.48886, df = 11.31, p-value = 0.6343
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-291.7323 185.4008
sample estimates:
mean in group Female mean in group Male
398.6760 451.8417 # CD
ind.t.test1<- t.test(CDHeartSignal12 ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: CDHeartSignal12 by Gender
t = 2.985, df = 12.586, p-value = 0.01087
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
1.754432 11.059922
sample estimates:
mean in group Female mean in group Male
1.335965 -5.071212 ind.t.test1<- t.test(CDHeartUnsig12 ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: CDHeartUnsig12 by Gender
t = 1.3974, df = 16.275, p-value = 0.1811
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.9919762 4.8444482
sample estimates:
mean in group Female mean in group Male
0.07017544 -1.85606061 ind.t.test1<- t.test(HrSigRecovMean ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: HrSigRecovMean by Gender
t = -1.3622, df = 25.949, p-value = 0.1849
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.727776 1.162266
sample estimates:
mean in group Female mean in group Male
-2.22214912 0.06060606 ind.t.test1<- t.test(HrUnSigRecovMean ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: HrUnSigRecovMean by Gender
t = -1.0099, df = 14.88, p-value = 0.3287
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.647962 2.018177
sample estimates:
mean in group Female mean in group Male
-1.4133772 0.4015152 ind.t.test1<- t.test(CDSkinSignal12 ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: CDSkinSignal12 by Gender
t = 1.531, df = 29.165, p-value = 0.1365
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.01332112 0.09272163
sample estimates:
mean in group Female mean in group Male
0.09148624 0.05178598 ind.t.test1<- t.test(CDSkinUnsig12 ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: CDSkinUnsig12 by Gender
t = -0.40722, df = 14.891, p-value = 0.6896
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.07488545 0.05087381
sample estimates:
mean in group Female mean in group Male
-0.009687410 0.002318409 ind.t.test1<- t.test(ScSigRecovMean ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: ScSigRecovMean by Gender
t = 0.42775, df = 18.515, p-value = 0.6738
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.1732722 0.2620893
sample estimates:
mean in group Female mean in group Male
0.2408939 0.1964854 ind.t.test1<- t.test(ScUnSigRecovMean ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: ScUnSigRecovMean by Gender
t = 0.3093, df = 17.063, p-value = 0.7608
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.1332284 0.1790173
sample estimates:
mean in group Female mean in group Male
0.1995503 0.1766558 Survey
# SRP
ind.t.test1<- t.test(SRPTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPTotalScore by Gender
t = -2.424, df = 46.285, p-value = 0.01932
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-20.594560 -1.909799
sample estimates:
mean in group Female mean in group Male
138.9859 150.2381 ind.t.test1<- t.test(SRPIPMTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPIPMTotal by Gender
t = -1.6063, df = 49.122, p-value = 0.1146
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.5029328 0.6135968
sample estimates:
mean in group Female mean in group Male
37.12676 39.57143 ind.t.test1<- t.test(SRPCATotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPCATotal by Gender
t = -3.9353, df = 30.13, p-value = 0.0004535
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-10.978471 -3.477599
sample estimates:
mean in group Female mean in group Male
35.29577 42.52381 ind.t.test1<- t.test(SRPELSTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPELSTotal by Gender
t = -0.57104, df = 43.211, p-value = 0.5709
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.914022 2.745008
sample estimates:
mean in group Female mean in group Male
41.91549 43.00000 ind.t.test1<- t.test(SRPASBTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPASBTotal by Gender
t = -0.27844, df = 31.625, p-value = 0.7825
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.117561 3.127621
sample estimates:
mean in group Female mean in group Male
24.64789 25.14286 # ICU
ind.t.test1<- t.test(ICUTotScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUTotScore by Gender
t = -3.07, df = 40.837, p-value = 0.003796
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-7.665736 -1.581749
sample estimates:
mean in group Female mean in group Male
41.66197 46.28571 ind.t.test1<- t.test(ICUUncareTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUUncareTotalScore by Gender
t = -1.4295, df = 46.976, p-value = 0.1595
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-2.5816516 0.4367824
sample estimates:
mean in group Female mean in group Male
14.07042 15.14286 ind.t.test1<- t.test(ICUUnemoTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUUnemoTotal by Gender
t = -2.2486, df = 32.625, p-value = 0.03142
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.0838237 -0.2032319
sample estimates:
mean in group Female mean in group Male
12.38028 14.52381 # LSRP
ind.t.test1<- t.test(LevTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevTotalScore by Gender
t = -2.0005, df = 41.647, p-value = 0.05199
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-6.54718941 0.02941611
sample estimates:
mean in group Female mean in group Male
45.78873 49.04762 ind.t.test1<- t.test(LevPrimTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevPrimTotalScore by Gender
t = -2.1956, df = 33.799, p-value = 0.03509
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.2801499 -0.2034181
sample estimates:
mean in group Female mean in group Male
27.59155 30.33333 ind.t.test1<- t.test(LevSecTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevSecTotalScore by Gender
t = -0.70821, df = 47.259, p-value = 0.4823
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.9857677 0.9515625
sample estimates:
mean in group Female mean in group Male
18.19718 18.71429 # SSS
ind.t.test1<- t.test(SSSTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSTotalScore by Gender
t = -1.2108, df = 38.168, p-value = 0.2334
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.037142 1.015009
sample estimates:
mean in group Female mean in group Male
16.77465 18.28571 ind.t.test1<- t.test(SSSDISTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSDISTotal by Gender
t = 0.16959, df = 35.41, p-value = 0.8663
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.044363 1.234840
sample estimates:
mean in group Female mean in group Male
4.000000 3.904762 ind.t.test1<- t.test(SSSThrilTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSThrilTotal by Gender
t = -2.4666, df = 36.485, p-value = 0.01846
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-2.9422934 -0.2877535
sample estimates:
mean in group Female mean in group Male
5.718310 7.333333 Distributions of DVs
# Histogram function
histo <- function(df, var, title = "Histogram", xlab = "DV", ylab = "Frequency", col = "honeydew", border = "black", bins = 5){
df |>
ggplot(aes(x = {{var}})) +
geom_histogram(binwidth = bins, fill = col, color = border) +
labs(title = title, x = xlab, y = ylab)
}SRP Full
Normal = SRPTot, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal
# SRPTot
FSFSurveyT1 |>
histo(SRPTotalScore)
qqnorm(FSFSurveyT1$SRPTotalScore)
qqline(FSFSurveyT1$SRPTotalScore)
shapiro.test(FSFSurveyT1$SRPTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPTotalScore
W = 0.97854, p-value = 0.1335# SRP IPM
FSFSurveyT1 |>
histo(SRPIPMTotal)
qqnorm(FSFSurveyT1$SRPIPMTotal)
qqline(FSFSurveyT1$SRPIPMTotal)
shapiro.test(FSFSurveyT1$SRPIPMTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPIPMTotal
W = 0.99234, p-value = 0.8779# SRPCATotal
FSFSurveyT1 |>
histo(SRPCATotal)
qqnorm(FSFSurveyT1$SRPCATotal)
qqline(FSFSurveyT1$SRPCATotal)
shapiro.test(FSFSurveyT1$SRPCATotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPCATotal
W = 0.98428, p-value = 0.3365# SRPELSTotal
FSFSurveyT1 |>
histo(SRPELSTotal)
qqnorm(FSFSurveyT1$SRPELSTotal)
qqline(FSFSurveyT1$SRPELSTotal)
shapiro.test(FSFSurveyT1$SRPELSTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPELSTotal
W = 0.97861, p-value = 0.1352# SRPASBTotal
FSFSurveyT1 |>
histo(SRPASBTotal)
qqnorm(FSFSurveyT1$SRPASBTotal)
qqline(FSFSurveyT1$SRPASBTotal)
shapiro.test(FSFSurveyT1$SRPASBTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPASBTotal
W = 0.93314, p-value = 0.0001476ICU Full
Non-Normal = ICUTotScore, ICUUncareTotalScore, ICUUnemoTotal
# ICUtotal
FSFSurveyT1 |>
histo(ICUTotScore)
qqnorm(FSFSurveyT1$ICUTotScore)
qqline(FSFSurveyT1$ICUTotScore)
shapiro.test(FSFSurveyT1$ICUTotScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUTotScore
W = 0.96482, p-value = 0.01386# ICUUncare
FSFSurveyT1 |>
histo(ICUUncareTotalScore)
qqnorm(FSFSurveyT1$ICUUncareTotalScore)
qqline(FSFSurveyT1$ICUUncareTotalScore)
shapiro.test(FSFSurveyT1$ICUUncareTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUUncareTotalScore
W = 0.96571, p-value = 0.01598# Unemo
FSFSurveyT1 |>
histo(ICUUnemoTotal)
qqnorm(FSFSurveyT1$ICUUnemoTotal)
qqline(FSFSurveyT1$ICUUnemoTotal)
shapiro.test(FSFSurveyT1$ICUUnemoTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUUnemoTotal
W = 0.97151, p-value = 0.0413Lev full
Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore
# Leve tot
FSFSurveyT1 |>
histo(LevTotalScore)
qqnorm(FSFSurveyT1$LevTotalScore)
qqline(FSFSurveyT1$LevTotalScore)
shapiro.test(FSFSurveyT1$LevTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevTotalScore
W = 0.97816, p-value = 0.1254# lev prim
FSFSurveyT1 |>
histo(LevPrimTotalScore)
qqnorm(FSFSurveyT1$LevPrimTotalScore)
qqline(FSFSurveyT1$LevPrimTotalScore)
shapiro.test(FSFSurveyT1$LevPrimTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevPrimTotalScore
W = 0.96646, p-value = 0.01804# lev sec
FSFSurveyT1 |>
histo(LevSecTotalScore)
qqnorm(FSFSurveyT1$LevSecTotalScore)
qqline(FSFSurveyT1$LevSecTotalScore)
shapiro.test(FSFSurveyT1$LevSecTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevSecTotalScore
W = 0.97963, p-value = 0.1598SSS Full
Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSThrilTotal
# SSS total
FSFSurveyT1 |>
histo(SSSTotalScore)
qqnorm(FSFSurveyT1$SSSTotalScore)
qqline(FSFSurveyT1$SSSTotalScore)
shapiro.test(FSFSurveyT1$SSSTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSTotalScore
W = 0.97996, p-value = 0.1689# SSS dis
FSFSurveyT1 |>
histo(SSSDISTotal)
qqnorm(FSFSurveyT1$SSSDISTotal)
qqline(FSFSurveyT1$SSSDISTotal)
shapiro.test(FSFSurveyT1$SSSDISTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSDISTotal
W = 0.95666, p-value = 0.003886# SSSThrilTotal
FSFSurveyT1 |>
histo(SSSThrilTotal)
qqnorm(FSFSurveyT1$SSSThrilTotal)
qqline(FSFSurveyT1$SSSThrilTotal)
shapiro.test(FSFSurveyT1$SSSThrilTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSThrilTotal
W = 0.92751, p-value = 0.00007295Table 2 (Partial Correlations for Rest and SSST)
This table contain the baseline and SSST measures will partialing the correlations for gender, race, and age.
Full
HR baseline
#SRP
pcor.test(FSFHRT1$SRPTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02466179 0.8185544 -0.2300998 92 3 pearsonpcor.test(FSFHRT1$SRPIPMTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.001882713 0.9860294 -0.01756081 92 3 pearsonpcor.test(FSFHRT1$SRPCATotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.06532416 0.5430514 0.6106074 92 3 pearsonpcor.test(FSFHRT1$SRPELSTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.0644952 0.5481925 -0.6028263 92 3 pearsonpcor.test(FSFHRT1$SRPASBTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.003973727 0.9705183 -0.03706476 92 3 spearman# ICU
pcor.test(FSFHRT1$ICUTotScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.07816455 0.4665563 0.7313079 92 3 spearmanpcor.test(FSFHRT1$ICUUncareTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.05647354 0.5991254 0.5275921 92 3 spearmanpcor.test(FSFHRT1$ICUUnemoTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.03214773 0.7648858 0.3000091 92 3 spearman# Lev
pcor.test(FSFHRT1$LevTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03376021 0.7534601 0.3150738 92 3 pearsonpcor.test(FSFHRT1$LevPrimTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01071867 0.920588 0.09998288 92 3 spearmanpcor.test(FSFHRT1$LevSecTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02581214 0.8102459 -0.2408399 92 3 pearson# SSS
pcor.test(FSFHRT1$SSSTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.08032739 0.4542764 -0.751673 92 3 pearsonpcor.test(FSFHRT1$SSSDISTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.067002 0.5327185 0.6263606 92 3 spearmanpcor.test(FSFHRT1$SSSThrilTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1322897 0.216527 -1.244858 92 3 spearmanSC baseline
#SRP
pcor.test(FSFSCT1$SRPTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.174352 0.1083755 -1.622819 89 3 pearsonpcor.test(FSFSCT1$SRPIPMTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.002700979 0.9803091 -0.02475497 89 3 pearsonpcor.test(FSFSCT1$SRPCATotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1355034 0.2135123 -1.25347 89 3 pearsonpcor.test(FSFSCT1$SRPELSTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3132829 0.003313279 -3.023489 89 3 pearsonpcor.test(FSFSCT1$SRPASBTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.001921123 0.9859938 -0.01760741 89 3 spearman# ICU
pcor.test(FSFSCT1$ICUTotScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.008222469 0.9401053 -0.07536272 89 3 spearmanpcor.test(FSFSCT1$ICUUncareTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.006848178 0.9501019 -0.06276606 89 3 spearmanpcor.test(FSFSCT1$ICUUnemoTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1357248 0.2127582 1.255556 89 3 spearman# Lev
pcor.test(FSFSCT1$LevTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1115422 0.3065639 -1.028721 89 3 pearsonpcor.test(FSFSCT1$LevPrimTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08616251 0.4302205 -0.7926402 89 3 spearmanpcor.test(FSFSCT1$LevSecTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1560232 0.1514214 -1.447706 89 3 pearson# SSS
pcor.test(FSFSCT1$SSSTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2314476 0.03202051 -2.180458 89 3 pearsonpcor.test(FSFSCT1$SSSDISTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1013222 0.3532696 -0.9334373 89 3 spearmanpcor.test(FSFSCT1$SSSThrilTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05390731 0.6220414 -0.4947881 89 3 spearmanTable 3 (Partial Correlations for CD)
This table contain the CD measures will partialing the correlations for gender, race, and age.
Countdown
## HR Signaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.1478284 0.3268744 -0.9914763 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1936862 0.1971393 -1.309567 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1521182 0.3128734 -1.020919 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.03186352 0.8335001 -0.211466 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2259082 0.1311433 -1.538272 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2789601 0.06046473 -1.926905 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1337789 0.3754259 -0.8954376 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.3724346 0.01080672 -2.661957 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1156638 0.4440005 -0.7724108 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1938291 0.1968026 -1.310571 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.01422392 0.9252513 -0.09436033 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.03521542 0.8162728 -0.2337376 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01171918 0.9383861 0.07774159 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHeartSignal12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.02108854 0.8893643 0.1399167 49 3 spearman## HR Unsignaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 0.1249296 0.4080993 0.8352325 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.03193856 0.8331135 -0.2119646 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.2678968 0.07185722 1.844445 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1175145 0.4366937 0.784942 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.09002456 0.5518548 0.59959 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.05818488 0.7009085 0.3866098 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1256487 0.4053845 -0.8401175 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1743482 0.246519 1.174483 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.04795238 0.751653 -0.3184464 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.107369 0.4775626 -0.7163463 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.05017524 0.7405324 -0.3332446 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.08864951 0.5579703 0.5903587 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01897036 0.9004178 0.1258578 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHeartUnsig12, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.02568922 0.8654304 0.1704593 49 3 spearman## SC signaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.1117955 0.4646937 -0.7377165 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2965734 0.0479008 -2.036378 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.04198522 0.7842071 0.2755585 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.09491681 0.5351217 -0.625234 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1301221 0.3942403 0.8605841 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1275661 0.4036737 -0.8433972 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1011526 0.5085119 -0.6667215 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08455553 0.5807807 -0.5564605 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1471785 0.3346509 -0.9757399 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.09652675 0.5281874 -0.6359378 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1639674 0.2818021 -1.089958 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1398108 0.3596685 -0.9258943 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.00643533 0.9665345 0.04220015 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSkinSignal12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.03004106 0.8446925 -0.1970814 48 3 spearman## SC Unsignaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 0.01962957 0.8981601 0.1287445 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.01057337 0.945042 -0.06933808 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.05179782 0.7354239 -0.3401176 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.05571898 0.7162024 0.3659423 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08919825 0.560105 -0.5872529 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.09137439 0.5505339 -0.6016991 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.005551595 0.9711281 -0.0364048 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.01403822 0.9270749 -0.09206384 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1964881 0.1957882 -1.314075 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.139363 0.3612245 -0.9228702 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.05860487 0.702165 -0.3849595 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.01821874 0.9054452 0.1194881 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.07234514 0.6367348 -0.4756452 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSkinUnsig12, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.073025 0.6335622 0.4801389 48 3 spearmanRecovery Measures Partials
Heart Rate
## HR Signaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.05930941 0.695405 -0.3941079 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1146755 0.4479296 -0.7657227 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1527993 0.310686 -1.025599 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02160899 0.8866515 -0.1433713 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1370913 0.363609 0.9180286 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1940939 0.1961797 -1.312432 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01226065 0.9355451 0.08133407 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06332208 0.6758945 -0.4208758 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02641331 0.8616738 -0.1752672 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01396095 0.9266295 0.09261548 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.01672586 0.9121508 -0.1109623 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.008305349 0.9563135 -0.05509335 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.07393605 0.6253178 0.4917823 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$HrSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.06595398 0.6632098 0.4384439 49 3 spearman## HR Unsignaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.1963923 0.190832 -1.328593 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1222748 0.4182108 -0.8172113 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.09021249 0.5510215 -0.6008519 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3093815 0.03641917 -2.158085 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05705782 0.7064396 -0.3790964 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1228591 0.415973 -0.8211764 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2152969 0.1507343 -1.462414 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06759574 0.6553439 -0.4494073 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1607727 0.285806 -1.080501 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.08579742 0.5707555 0.5712221 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3226232 0.02875993 -2.260938 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.04999823 0.7414161 0.3320661 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.0875386 0.5629343 0.5829031 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$HrUnSigRecovMean, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0323961 0.8307574 -0.2150043 49 3 spearmanSkin Conductance
## SC signaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.2423723 0.1086808 -1.638187 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.196241 0.1963623 -1.312356 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06581811 0.6675135 -0.4325361 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2438939 0.1064111 -1.649119 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1427012 0.3497218 -0.9454304 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08836769 0.5637784 -0.5817415 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06865035 0.6540877 -0.451235 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01789238 0.9071315 0.117347 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1533109 0.3146754 -1.017354 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.008197381 0.9573788 0.05375563 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2465958 0.1024704 -1.668565 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06753204 0.6593763 -0.4438504 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.08232942 0.590815 0.5417091 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$ScSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.03133836 0.8380734 0.2056003 48 3 spearman## SC Unsignaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.233414 0.1227994 -1.574078 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1284775 0.4002952 -0.8495237 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02519517 0.8695074 -0.1652682 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2614933 0.08271926 -1.776541 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2506389 0.09678388 -1.69774 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1101793 0.4712145 -0.7269194 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1276759 0.4032659 -0.844135 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01666282 0.9134883 0.1092806 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.09580305 0.5312991 -0.6311256 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01120758 0.941751 0.07349764 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1985125 0.19113 -1.328166 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03311082 0.8290478 0.2172413 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.2239739 0.1391279 1.50698 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$ScUnSigRecovMean, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1659117 0.2760564 1.103246 48 3 spearmanFigures
Code for Fig 1
HR
pacman::p_load(ggtext, fontawesome, showtext, sysfonts, patchwork, ggrepel)
font_add_google("Roboto")
showtext_auto()custom_labels <- c(0, 25, 50, 75, 100, 125, 150, 175, 200, 225)
HRViz <- ggplot(data12, aes(x = time, y = value)) +
geom_line(color = "#A7C7E7") +
geom_point(color = "#FF964F", size = 1) +
geom_label_repel(data = subset(data12, time == 24),
aes(label = "Speech Task Begins"),
point.padding = 1,
nudge_x = 0.7,
nudge_y = -2,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
size = 3
) +
labs(title = "Social Stressor Task: Heart Rate",
x = "Time (Seconds)",
y = "Heart Rate (BPM)") +
theme_minimal() +
theme(plot.title = element_text(family = "Roboto",
size = 12,
hjust = 0.5,
face = "bold",
margin = margin(b = 6)),
axis.title.y = element_text(family = "Roboto",
size = 10),
axis.title.x = element_text(family = "Roboto",
size = 10)) +
scale_x_continuous(breaks = seq(0, max(data12$time), by = 5),
labels = custom_labels) +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1)),
breaks = seq(floor(min(data12$value)),
ceiling(max(data12$value)),
by = 1)) +
coord_cartesian(ylim = c((min(data12$value)) - 0.15, (max(data12$value)) + 0.15)) Skin Conductance
custom_labels <- c(0, 25, 50, 75, 100, 125, 150, 175, 200, 225)
SCViz <- ggplot(data123, aes(x = time,
y = value)) +
geom_line(color = "#A7C7E7") +
geom_point(color = "#FF964F",
size = 1) +
geom_label_repel(data = subset(data123,
time == 24),
aes(label = "Speech Task Begins"),
point.padding = 1,
nudge_x = 1,
nudge_y = -0.3,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
size = 3
) +
labs(title = "Social Stressor Task: Skin Conductance",
x = "Time (Seconds)",
y = "Skin Conductance Level (μS)") +
theme_minimal() +
theme(plot.title = element_text(family = "Roboto",
size = 12,
hjust = 0.5,
face = "bold",
margin = margin(b = 6)),
axis.title.y = element_text(family = "Roboto",
size = 10),
axis.title.x = element_text(family = "Roboto",
size = 10)
) +
scale_x_continuous(breaks = seq(0, max(data123$time), by = 5),
labels = custom_labels)Plots (Fig 1)
HRSCStacked <- HRViz / SCViz
HRSCStacked
Code for Fig 2
HR
CDHRVizStack <- ggplot(CDGraphHR,
aes(x = time,
y = value,
group = group,
color = group)) +
geom_line() +
geom_label_repel(
data = subset(CDGraphHR, time == 6 & group == "Signaled"),
aes(label = "Countdown Begins"),
point.padding = 1,
nudge_x = 4.0,
nudge_y = 0.6,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
color = "black",
size = 2,
show.legend = FALSE
) +
geom_label_repel(
data = subset(CDGraphHR, time == 18 & group == "Signaled"),
aes(label = "Noise Blast"),
point.padding = 1,
nudge_x = 4,
nudge_y = 0.5,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
color = "black",
size = 2,
show.legend = FALSE
) +
geom_vline(xintercept = 6, linetype = "solid", color = "#F5F5F5") +
geom_vline(xintercept = 18, linetype = "solid", color = "#F5F5F5") +
scale_color_manual(values = c("Signaled" = "#A7C7E7",
"Unsignaled" = "#E7BFA7"),
labels = c("Signaled", "Unsignaled")) +
geom_point(color = "#FF964F", size = 1) +
labs(title = "Countdown Task: Heart Rate",
x = "Time (Seconds)",
y = "Heart Rate (BPM)") +
theme_minimal() +
theme(plot.title = element_text(family = "Roboto",
size = 12,
hjust = 0.5,
face = "bold",
margin = margin(b = 6)),
axis.title.y = element_text(family = "Roboto",
size = 10),
axis.title.x = element_text(family = "Roboto",
size = 10),
legend.title = element_blank(),
legend.text = element_text(family = "Roboto",
size = 10),
legend.position = "top"
) +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1)),
breaks = seq(floor(min(CDGraphHR$value)),
ceiling(max(CDGraphHR$value)),
by = 1)) +
coord_cartesian(ylim = c((min(CDGraphHR$value)) - 0.15, (max(CDGraphHR$value)) + 0.15)) SC
CDSCVizStack <- ggplot(CDGraphSC,
aes(x = time,
y = value,
group = group,
color = group)) +
geom_line() +
geom_label_repel(
data = subset(CDGraphSC, time == 6 & group == "Unsignaled"),
aes(label = "Countdown Begins"),
point.padding = 1,
nudge_x = 3.2,
nudge_y = 0.3,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
color = "black",
size = 2,
show.legend = FALSE
) +
geom_label_repel(
data = subset(CDGraphSC, time == 18 & group == "Unsignaled"),
aes(label = "Noise Blast"),
point.padding = 1,
nudge_x = -3,
nudge_y = 0.3,
arrow = arrow(length = unit(0.015, "npc")),
family = "Roboto",
color = "black",
size = 2,
show.legend = FALSE
) +
geom_vline(xintercept = 6, linetype = "solid", color = "#F5F5F5") +
geom_vline(xintercept = 18, linetype = "solid", color = "#F5F5F5") +
scale_color_manual(values = c("Signaled" = "#A7C7E7",
"Unsignaled" = "#E7BFA7"),
labels = c("Signaled", "Unsignaled")) +
geom_point(color = "#FF964F", size = 1) +
labs(title = "Countdown Task: Skin Conductance Level",
x = "Time (Seconds)",
y = "Skin Conductance Level (μS)") +
theme_minimal() +
theme(plot.title = element_text(family = "Roboto",
size = 12,
hjust = 0.5,
face = "bold",
margin = margin(b = 6)),
axis.title.y = element_text(family = "Roboto",
size = 10),
axis.title.x = element_text(family = "Roboto",
size = 10),
legend.title = element_blank(),
legend.text = element_text(family = "Roboto",
size = 10),
legend.position = "top"
) Plots (Fig 2)
CDHRSCStacked <- CDHRVizStack / CDSCVizStack
CDHRSCStacked
Social Stressor
Function 1
Creates a new data frame with the mean of every 5 columns.